The mechanisms behind the evolution of complex genomic amplifications in cancer have remained largely unclear. Using whole-genome sequencing data of the pediatric tumor neuroblastoma, we here identified a type of amplification, termed ‘seismic amplification’, that is characterized by multiple rearrangements and discontinuous copy number levels. Overall, seismic amplifications occurred in 9.9% (274 of 2,756) of cases across 38 cancer types, and were associated with massively increased copy numbers and elevated oncogene expression. Reconstruction of the development of seismic amplification showed a stepwise evolution, starting with a chromothripsis event, followed by formation of circular extrachromosomal DNA that subsequently underwent repetitive rounds of circular recombination. The resulting amplicons persisted as extrachromosomal DNA circles or had reintegrated into the genome in overt tumors. Together, our data indicate that the sequential occurrence of chromothripsis and circular recombination drives oncogene amplification and overexpression in a substantial fraction of human malignancies.
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Patient WGS data have been deposited at the European Genome-phenome Archive (https://ega-archive.org) under study accession numbers EGAS00001001308 and https://ega-archive.org/studies/EGAS00001005424. Due to the sensitive nature of these patient datasets, the WGS data is subject to approval by the data provider. Please see the corresponding EGA data access committee (DAC) for more details on the procedure (https://ega-archive.org/dacs/EGAC00001000361). Cell line sequencing data have been deposited at the European Nucleotide Archive (https://www.ebi.ac.uk/ena/) under study accession number https://www.ebi.ac.uk/ena/data/view/PRJEB45367, without access restrictions. All PCAWG sample, copy number and rearrangement data as well as RNA sequencing data were downloaded from the ICGC data portal at https://dcc.icgc.org/releases/PCAWG/.
The R and C++ code used for detection of seismic amplification, simulation of chromothripsis, seismic and BFB amplification, the DM birth–death model and microhomology calling is available for download at https://github.com/seismicon/SeismicAmplification. The rearrangement calling algorithm SoReCa can be downloaded from http://www.uni-koeln.de/med-fak/soreca/soreca.tgz. The copy number analysis algorithm Sclust is available at http://www.uni-koeln.de/med-fak/sclust/Sclust.tgz.
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).
Biedler, J. L. & Spengler, B. A. Metaphase chromosome anomaly: association with drug resistance and cell-specific products. Science 191, 185–187 (1976).
Fan, Y. et al. Frequency of double minute chromosomes and combined cytogenetic abnormalities and their characteristics. J. Appl. Genet. 52, 53–59 (2011).
Koche, R. P. et al. Extrachromosomal circular DNA drives oncogenic genome remodeling in neuroblastoma. Nat. Genet. 52, 29–34 (2020).
L’Abbate, A. et al. Genomic organization and evolution of double minutes/homogeneously staining regions with MYC amplification in human cancer. Nucleic Acids Res. 42, 9131–9145 (2014).
Turner, K. M. et al. Extrachromosomal oncogene amplification drives tumour evolution and genetic heterogeneity. Nature 543, 122–125 (2017).
Maris, J. M., Hogarty, M. D., Bagatell, R. & Cohn, S. L. Neuroblastoma. Lancet 369, 2106–2120 (2007).
Moreau, L. A. et al. Does MYCN amplification manifested as homogeneously staining regions at diagnosis predict a worse outcome in children with neuroblastoma? A Children’s Oncology Group study. Clin. Cancer Res. 12, 5693–5697 (2006).
Garsed, D. W. et al. The architecture and evolution of cancer neochromosomes. Cancer Cell 26, 653–667 (2014).
Li, Y. et al. Patterns of somatic structural variation in human cancer genomes. Nature 578, 112–121 (2020).
Stephens, P. J. et al. Complex landscapes of somatic rearrangement in human breast cancer genomes. Nature 462, 1005–1010 (2009).
Selvarajah, S. et al. The breakage-fusion-bridge (BFB) cycle as a mechanism for generating genetic heterogeneity in osteosarcoma. Chromosoma 115, 459–467 (2006).
Verhaak, R. G. W., Bafna, V. & Mischel, P. S. Extrachromosomal oncogene amplification in tumour pathogenesis and evolution. Nat. Rev. Cancer 19, 283–288 (2019).
Korbel, J. O. & Campbell, P. J. Criteria for inference of chromothripsis in cancer genomes. Cell 152, 1226–1236 (2013).
Stephens, P. J. et al. Massive genomic rearrangement acquired in a single catastrophic event during cancer development. Cell 144, 27–40 (2011).
Ly, P. et al. Chromosome segregation errors generate a diverse spectrum of simple and complex genomic rearrangements. Nat. Genet. 51, 705–715 (2019).
Otto, T. & Sicinski, P. Cell cycle proteins as promising targets in cancer therapy. Nat. Rev. Cancer 17, 93–115 (2017).
Vogelstein, B. & Kinzler, K. W. Cancer genes and the pathways they control. Nat. Med. 10, 789–799 (2004).
ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. Nature 578, 82–93 (2020).
Santarius, T., Shipley, J., Brewer, D., Stratton, M. R. & Cooper, C. S. A census of amplified and overexpressed human cancer genes. Nat. Rev. Cancer 10, 59–64 (2010).
Li, Y. et al. Constitutional and somatic rearrangement of chromosome 21 in acute lymphoblastic leukaemia. Nature 508, 98–102 (2014).
Matsui, A., Ihara, T., Suda, H., Mikami, H. & Semba, K. Gene amplification: mechanisms and involvement in cancer. Biomol. Concepts 4, 567–582 (2013).
L′Abbate, A. et al. MYC-containing amplicons in acute myeloid leukemia: genomic structures, evolution, and transcriptional consequences. Leukemia 32, 2152–2166 (2018).
Cortés-Ciriano, I. et al. Comprehensive analysis of chromothripsis in 2,658 human cancers using whole-genome sequencing. Nat. Genet. 52, 331–341 (2020).
Shoshani, O. et al. Chromothripsis drives the evolution of gene amplification in cancer. Nature 591, 137–141 (2021).
Shimizu, N., Shingaki, K., Kaneko-Sasaguri, Y., Hashizume, T. & Kanda, T. When, where and how the bridge breaks: anaphase bridge breakage plays a crucial role in gene amplification and HSR generation. Exp. Cell. Res. 302, 233–243 (2005).
Zakov, S., Kinsella, M. & Bafna, V. An algorithmic approach for breakage-fusion-bridge detection in tumor genomes. Proc. Natl Acad. Sci. USA 110, 5546–5551 (2013).
Kim, H. et al. Extrachromosomal DNA is associated with oncogene amplification and poor outcome across multiple cancers. Nat. Genet. 52, 891–897 (2020).
Garsed, D. W., Holloway, A. J. & Thomas, D. M. Cancer-associated neochromosomes: a novel mechanism of oncogenesis. Bioessays 31, 1191–1200 (2009).
Vasmatzis, G. et al. Chromoanasynthesis is a common mechanism that leads to ERBB2 amplifications in a cohort of early stage HER2(+) breast cancer samples. BMC Cancer 18, 738 (2018).
Gibaud, A. et al. Extrachromosomal amplification mechanisms in a glioma with amplified sequences from multiple chromosome loci. Hum. Mol. Genet. 19, 1276–1285 (2010).
Campbell, P. J. et al. Identification of somatically acquired rearrangements in cancer using genome-wide massively parallel paired-end sequencing. Nat. Genet. 40, 722–729 (2008).
Barra, V. & Fachinetti, D. The dark side of centromeres: types, causes and consequences of structural abnormalities implicating centromeric DNA. Nat. Commun. 9, 4340 (2018).
Benner, S. E., Wahl, G. M. & Von Hoff, D. D. Double minute chromosomes and homogeneously staining regions in tumors taken directly from patients versus in human tumor cell lines. Anticancer Drugs 2, 11–25 (1991).
Beird, H. C. et al. Genomic profiling of dedifferentiated liposarcoma compared to matched well-differentiated liposarcoma reveals higher genomic complexity and a common origin. Cold Spring Harb Mol Case Stud 4, a002386 (2018).
Pedeutour, F. et al. Structure of the supernumerary ring and giant rod chromosomes in adipose tissue tumors. Genes Chromosomes Cancer 24, 30–41 (1999).
Macintyre, G. et al. Copy number signatures and mutational processes in ovarian carcinoma. Nat. Genet. 50, 1262–1270 (2018).
Hellinger, E. Neue Begründung der Theorie quadratischer Formen von unendlichvielen Veränderlichen. J. Reine Angew. Math. 136, 210–271 (1909).
Alexandrov, L. B. et al. The repertoire of mutational signatures in human cancer. Nature 578, 94–101 (2020).
Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).
Swanton, C., McGranahan, N., Starrett, G. J. & Harris, R. S. APOBEC enzymes: mutagenic fuel for cancer evolution and heterogeneity. Cancer Discov. 5, 704–712 (2015).
Nik-Zainal, S. et al. Mutational processes molding the genomes of 21 breast cancers. Cell 149, 979–993 (2012).
Roberts, S. A. et al. An APOBEC cytidine deaminase mutagenesis pattern is widespread in human cancers. Nat. Genet. 45, 970–976 (2013).
Maciejowski, J., Li, Y., Bosco, N., Campbell, P. J. & de Lange, T. Chromothripsis and kataegis induced by telomere crisis. Cell 163, 1641–1654 (2015).
Chan, K. & Gordenin, D. A. Clusters of multiple mutations: incidence and molecular mechanisms. Annu. Rev. Genet. 49, 243–267 (2015).
Elango, R. et al. Repair of base damage within break-induced replication intermediates promotes kataegis associated with chromosome rearrangements. Nucleic Acids Res. 47, 9666–9684 (2019).
Pfeiffer, P., Goedecke, W. & Obe, G. Mechanisms of DNA double-strand break repair and their potential to induce chromosomal aberrations. Mutagenesis 15, 289–302 (2000).
Hicks, J. et al. Novel patterns of genome rearrangement and their association with survival in breast cancer. Genome Res. 16, 1465–1479 (2006).
Hadi, K. et al. Distinct classes of complex structural variation uncovered across thousands of cancer genome graphs. Cell 183, 197–210 e132 (2020).
Helmsauer, K. et al. Enhancer hijacking determines extrachromosomal circular MYCN amplicon architecture in neuroblastoma. Nat. Commun. 11, 5823 (2020).
Wu, S. et al. Circular ecDNA promotes accessible chromatin and high oncogene expression. Nature 575, 699–703 (2019).
Rausch, T. et al. Genome sequencing of pediatric medulloblastoma links catastrophic DNA rearrangements with TP53 mutations. Cell 148, 59–71 (2012).
Zhang, C. Z. et al. Chromothripsis from DNA damage in micronuclei. Nature 522, 179–184 (2015).
Bailey, C., Shoura, M. J., Mischel, P. S. & Swanton, C. Extrachromosomal DNA-relieving heredity constraints, accelerating tumour evolution. Ann. Oncol. 31, 884–893 (2020).
Beverley, S. M., Coderre, J. A., Santi, D. V. & Schimke, R. T. Unstable DNA amplifications in methotrexate-resistant Leishmania consist of extrachromosomal circles which relocalize during stabilization. Cell 38, 431–439 (1984).
Schimke, R. T. Gene amplification in cultured animal cells. Cell 37, 705–713 (1984).
Bignell, G. R. et al. Architectures of somatic genomic rearrangement in human cancer amplicons at sequence-level resolution. Genome Res. 17, 1296–1303 (2007).
Corvi, R., Savelyeva, L., Amler, L., Handgretinger, R. & Schwab, M. Cytogenetic evolution of MYCN and MDM2 amplification in the neuroblastoma LS tumour and its cell line. Eur. J. Cancer 31A, 520–523 (1995).
Peifer, M. et al. Telomerase activation by genomic rearrangements in high-risk neuroblastoma. Nature 526, 700–704 (2015).
Cun, Y., Yang, T. P., Achter, V., Lang, U. & Peifer, M. Copy-number analysis and inference of subclonal populations in cancer genomes using Sclust. Nat. Protoc. 13, 1488–1501 (2018).
Fang, L. et al. LinkedSV for detection of mosaic structural variants from linked-read exome and genome sequencing data. Nat. Commun. 10, 5585 (2019).
Dentro, S. C. et al. Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes. Cell 184, 2239–2254 e2239 (2021).
CNV Overview (Catalog of Somatic Mutations in Cancer, accessed 20 March, 2020) https://cancer.sanger.ac.uk/cosmic/help/cnv/overview
We thank M. Dobbelstein for providing the cell lines 93T449 and 94T778 and J. Schulte for providing the cell line CLB-GA. We would also like to thank T. Acht, A. Schienke, M. Hagedorn, D. Schmitz and S. Hippler for their technical assistance with FISH, G-banding and m-FISH, D. Nöhrling for her contribution to rearrangement validation and H. C. Reinhardt for critical reading of the manuscript. We further thank C. Becker and M. Franitza for their support with linked-read sequencing and Bionano optical mapping. We thank the CECAD Imaging Facility for their support with microscopy. This work was funded through the Else Kröner-Fresenius Stiftung (2016-Kolleg-19 to C.R.), the Deutsche Forschungsgemeinschaft (DFG; grant no. BA 6984/1-1 to C.B., and FI 1926/2-1 to M.F.), the Förderverein für Krebskranke Kinder e.V. Köln (endowed chair to M.F.), the Fördergesellschaft Kinderkrebs-Neuroblastom-Forschung e.V. (to M.F.), the German Cancer Aid (Mildred-Scheel professorship to M.P.), the German Ministry of Science and Education (BMBF) as part of the e:Med initiative (grant no. 01ZX1303 and 01ZX1603 to R.K.T., M.P, and M.F.; grant no. 01ZX1901 to R.K.T. and M.P; grant no. 01ZX1307 and 01ZX1607 to M.F.), the DFG as part of the KFO286 to M.P. and the SFB1399 (to R.K.T., M.P., and M.F.). Additional support was received by the DFG Research Infrastructure as part of the Next Generation Sequencing Competence Network (project 423957469). NGS analyses were carried out at the production site WGGC Cologne.
The authors declare no competing interests.
Peer review information Nature Genetics thanks P. Van Loo, G. Macintyre and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Copy number and rearrangement profiles of seismic amplification in five primary neuroblastoma samples (NB-6 and NB-8-11) and three neuroblastoma cell lines. Copy number range is indicated for each sample on y axis.
Workflow for the detection of seismic amplifications from whole-genome sequencing data. CN, copy number; RA, rearrangement; NB, neuroblastoma.
(a) Confocal microscopy images of FISH analyses using distinct probes covering three hotspot regions of seismic amplification in neuroblastoma (that is, loci of MYCN, CDK4, and MDM2). Seismic amplification presented as clustered signals, indicating HSR (homogeneously staining regions) or NC (neochromosomes), or as extensively spread signals, indicating DM, both in interphase nuclei of tumor samples and in metaphase spreads of cell lines. Classification as DM, HSR or NC is indicated on respective images, if differentiation between extra- and intrachromosomal amplification was possible. Scale bars, 10 μm. CEP2 and CEP12, centromere protein of chromosome 2 and 12, respectively; XCP12, whole chromosome 12 paint. Each experiment was repeated at least two times independently. (b) Multicolor FISH (left) and corresponding G-banding (middle) of metaphase spreads of cell line NGP. White/black arrowheads indicate a HSR harboring chromosome 12 material integrated into chromosome 12; white/black asterisks indicate a HSR harboring chromosome 2 material integrated into chromosome 4. Fluorescence R-banding of metaphase spreads of cell line LS (right), corresponding to m-FISH analysis of Fig. 2f. Two neochromosomes harboring seismic amplicons are indicated by white arrowheads.
Extended Data Fig. 4 Genomic structure of contigs derived from optical mapping data of seismic amplicons in cell line NGP.
Visualization of optical mapping data of seismic amplicons in neuroblastoma cell line NGP. Contigs derived from the amplicon harboring rearranged genomic fragments of affected chromosomes (bottom) are shown in comparison to the copy number and rearrangement profile inferred from WGS data (top). Black arrows in the contigs indicate orientation of genomic fragments. CN, copy number; RA, rearrangement.
Extended Data Fig. 5 Genomic structure of contigs derived from optical mapping data of seismic amplicons in cell line LS.
Visualization of optical mapping data of seismic amplicons in neuroblastoma cell line LS. Contigs derived from the amplicon harboring rearranged genomic fragments of affected chromosomes (bottom) are shown in comparison to the copy number and rearrangement profile inferred from WGS data (top). Black arrows in the contigs indicate orientation of genomic fragments. CN, copy number; RA, rearrangement.
(a) Log10 ratio of number of internal rearrangements versus the number of segments in seismic amplicons of the pan-cancer cohort (n = 304; P = 1.014×10-82). Median copy numbers in individual amplicons are indicated by color code. (b) Distribution of rearrangement types occurring in internal and flanking rearrangements of seismic amplicons (n = 304). Boxplots show the median, first and third quartile (boxes), with whiskers indicating the minimum and maximum of the data within 1.5x the interquartile range. (c) Fraction of amplicons harboring >25% foldback inversions, indicating BFB24, in seismic versus other amplifications (P = 2.274×10-14). (d) Log10 ratio of the number of internal rearrangements classified as foldback inversions versus the number of segments in seismic amplicons of the pan-cancer cohort (n = 233; P = 1.386×10-17). (e) Log10 ratio of median read numbers covering internal rearrangements versus foldback inversions in seismic amplicons. (f) Log10 ratio of median read numbers covering flanking rearrangements versus foldback inversions in seismic amplicons. (g) Fraction of seismic amplicons presenting as putative DM/HSR and seismic amplicons presenting as putative NC across cancer types, according to involvement of <3 or ≥3 chromosomes in the amplicons, respectively. (h) Fraction of seismic amplicons overlapping with chromothriptic regions that also cover centromeres in putative DM/HSR and NC (P = 1.628×10-6). (i) Fraction of seismic amplicons comprising rearrangements to telomeric regions according to classification into putative DM/HSR and NC (P = 3.979×10-11). RA, rearrangement; Ct, chromothripsis; HSR, homogeneously staining regions; NC, neochromosomes; amp, amplification. Correlations and P values in a and d were based on Pearson’s product moment correlation coefficient. P values in c, h and i were calculated using a one-sided Fisher’s exact test.
(a) Schema of computational simulations of different scenarios potentially involved in the evolution of seismic amplification: (1) Chromothripsis followed by BFB, (2) BFB followed by chromothripsis, and (3) Chromothripsis followed by circular recombination. Parameter variations in distinct simulations are indicated in gray boxes. (b) Distribution of rearrangement types in computational simulations of scenario (3) with 20 or 30 cycles and 90% two crossing-overs (n = 1,800 amplicons). Boxplots show the median, first and third quartile (boxes), with whiskers indicating the minimum and maximum of the data within 1.5x the interquartile range. (c) Comparison of the distribution of copy number signatures of observed seismic amplifications and distinct simulated amplification scenarios in a T-SNE plot. Color codes indicate level of complexity according to the number of segments of seismic amplicons, number of BFB cycles of scenarios (1) and (2), and number of cycles of circular recombination of scenario (3). (d) The same T-SNE plot as in (c), indicating the level of complexity according to the number of segments of seismic amplicons, and the fraction of two crossing-over events during circular recombination in scenario (3). Ct, chromothripsis; BP, breakpoints; circ. rec., circular recombination; BFB, breakage-fusion-bridge.
Extended Data Fig. 8 Effect of variation of the internal rearrangement threshold on the detection of seismic amplifications.
(a) Number of seismic amplicons detected in the neuroblastoma cohort at varying thresholds for the number of internal rearrangements. (b) Number of seismic amplicons detected in the PCAWG cohort at varying thresholds for the number of internal rearrangements. (c) Number of seismic amplicons detected at varying thresholds for the number of internal rearrangements in simulation scenarios (1) (left), (2) (middle), and (3) (right). (d) Comparison of the distribution of copy number signatures of observed seismic amplifications and distinct simulated amplification scenarios in a T-SNE plot. Color codes indicate the distinct simulation scenarios (colored) and observed seismic amplifications according to different thresholds (gray scale). No., number; BFB, breakage-fusion-bridge; RA, rearrangement; circ. recombination, circular recombination.
Extended Data Fig. 9 Assessment of mutational signatures, kataegis and homologous sequences in seismic amplifications.
(a) Single-base substitution (SBS), double-base substitution (DBS) and small insertion and deletion (ID) signatures across cancer types of the pan-cancer cohort in cases with (pink) versus without seismic amplification (gray). N.s., not significant; *, P < 0.05; **, P < 0.01; ***, P < 0.001. P values were calculated using a two-sided Fisher’s exact test and Benjamini–Hochberg corrected. (b) Association of kataegis, seismic amplification and chromothripsis. Fraction of patient samples bearing kataegis clusters in cases with seismic amplification (n = 271; cell lines not included), chromothripsis lacking seismic amplification (n = 200; random subset), and samples lacking both (n = 400; random subset). The fraction of cases harboring kataegis clusters inside and outside seismic amplification and chromothripsis regions is indicated. P = 1.726×10-4 seismic vs non-Ct; P = 1.351×10-6 seismic vs Ct. P values were based on kataegis counts inside regions and were calculated using a one-sided Fisher’s exact test. Ct, chromothripsis. (c) Allelic frequencies (AF) of C > T and C > G mutations in kataegis clusters of seismic amplicons. (d) Location of kataegis clusters within seismic amplicons in relation to breakpoints of internal and flanking rearrangements. (e) Fraction of breakpoints showing microhomologies ≤10 bp and (f) microhomologies ≥10 bp in flanking and internal rearrangements of seismic amplifications, in rearrangements associated with chromothripsis, other amplifications, and nonamplified structural alterations, and in randomly distributed simulated rearrangements. (g) Occurrence of larger repeats in internal (n = 18,581) and flanking rearrangements (n = 2,223) of seismic amplifications, in rearrangements of other amplifications (n = 16,863), and in rearrangements of non-amplified regions (n = 285,173) in the pan-cancer cohort. The enrichment is computed as a fold-change between the actual counts and 500 random permutations of the second rearrangement breakpoint. Data are presented as mean values + /- three standard deviations. LTR, long terminal repeat; LINE, long interspersed nuclear element; SINE, short interspersed nuclear element; amp, amplification.
Extended Data Fig. 10 Temporal evolution of seismic amplification profiles in a population of extrachromosomal DNA circles.
(a) Correlation of average seismic amplification copy number profiles of a population of DNA circles to the endpoint of this simulation over time. Simulation of seismic amplifications in a population of DNA circles was performed in a birth–death model as described in the Methods. Dashed lines indicate correlation after distinct timepoints, referred to in (b) and (c), corresponding to average numbers of circular recombination cycles. (b) Example of copy number profiles after various average numbers of circular recombination cycles in a population of DNA circles in the birth-death model. (c) Development of copy number signatures with increasing numbers of average circular recombination cycles in the birth-death model. Sig, signature.
Supplementary Table 1. Sample characteristics. Supplementary Table 2. Details of seismic amplicon structure. Supplementary Table 3. Details of rearrangements associated to seismic amplicons in cell lines and neuroblastoma samples. Supplementary Table 4. Hellinger distances of computational simulations.
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Rosswog, C., Bartenhagen, C., Welte, A. et al. Chromothripsis followed by circular recombination drives oncogene amplification in human cancer. Nat Genet (2021). https://doi.org/10.1038/s41588-021-00951-7