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Chromothripsis followed by circular recombination drives oncogene amplification in human cancer

Nature Genetics volume 53, pages 1673–1685 (2021)Cite this article

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Abstract

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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Fig. 1: Identification of seismic amplification in neuroblastoma.
Fig. 2: Cytogenetic status of seismic amplification in neuroblastoma.
Fig. 3: Prevalence and characteristics of seismic amplification in human cancer.
Fig. 4: Association of seismic amplification and chromothripsis in human cancer.
Fig. 5: Circular recombination promotes genomic amplification in cancer cells.
Fig. 6: Model of the development of seismic amplification in cancer.
Fig. 7: Validation of the evolutionary model of seismic amplification by simulation.
Fig. 8: Initiation and termination of circular recombination.

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Data availability

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/.

Code availability

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.

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Acknowledgements

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.

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Author notes

  1. These authors contributed equally: Carolina Rosswog, Christoph Bartenhagen.

Authors and Affiliations

  1. Department of Experimental Pediatric Oncology, University Children’s Hospital of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany

    Carolina Rosswog, Christoph Bartenhagen, Anne Welte, Yvonne Kahlert, Nadine Hemstedt, Witali Lorenz, Sandra Ackermann, Falk Hertwig & Matthias Fischer

  2. Center for Molecular Medicine Cologne (CMMC), University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany

    Carolina Rosswog, Christoph Bartenhagen, Anne Welte, Yvonne Kahlert, Maria Cartolano, Sandra Ackermann, Peter Nürnberg, Martin Peifer & Matthias Fischer

  3. Else Kröner Forschungskolleg Clonal Evolution in Cancer, University Hospital Cologne, Cologne, Germany

    Carolina Rosswog

  4. Institute of Pathology, University of Luebeck and University Hospital Schleswig-Holstein, Campus Luebeck, Luebeck, Germany

    Sven Perner & Wenzel Vogel

  5. Pathology Research Center Borstel, Leibniz Lung Center, Borstel, Germany

    Sven Perner & Wenzel Vogel

  6. Cologne Center for Genomics (CCG), University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany

    Janine Altmüller & Peter Nürnberg

  7. Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Core Facility Genomics, Berlin, Germany

    Janine Altmüller

  8. Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany

    Janine Altmüller

  9. Department of Human Genetics, Hannover Medical School (MHH), Hannover, Germany

    Gudrun Göhring

  10. Department of Internal Medicine, University of Cologne, Cologne, Germany

    Esther Lilienweiss

  11. European Molecular Biology Laboratory Genome Biology Unit, Heidelberg, Germany

    Adrian M. Stütz & Jan O. Korbel

  12. Department of Translational Genomics, Medical Faculty, University of Cologne, Cologne, Germany

    Roman K. Thomas & Martin Peifer

  13. Department of Pathology, University of Cologne, Cologne, Germany

    Roman K. Thomas

  14. German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany

    Roman K. Thomas

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  1. Carolina Rosswog
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Contributions

C.R., C.B., J.O.K., R.K.T., M.P. and M.F. conceptualized the work. C.R., C.B., M.P. and M.F. developed the methodology. C.R., C.B., A.W., Y.K., N.H., W.L., S.A., A.M.S., S.P., W.V., F.H., J.A., P.N., G.G. and E.L. performed the investigations. C.R., C.B., M.C. and M.P. carried out the formal analysis. C.R., C.B., R.K.T., M.P. and M.F. acquired funding. M.P. and M.F. were responsible for resources. M.F. supervised the work. C.R., C.B., M.P. and M.F. were responsible for visualization. C.R., C.B., M.P. and M.F. wrote the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Martin Peifer or Matthias Fischer.

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Competing interests

The authors declare no competing interests.

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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.

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Extended data

Extended Data Fig. 1 Seismic amplification in neuroblastoma.

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.

Extended Data Fig. 2 Detection of seismic amplification.

Workflow for the detection of seismic amplifications from whole-genome sequencing data. CN, copy number; RA, rearrangement; NB, neuroblastoma.

Extended Data Fig. 3 Cytogenetic status of seismic amplification in 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.

Extended Data Fig. 6 Characteristics of rearrangements occurring in seismic amplifications.

(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.

Extended Data Fig. 7 Computational validation of the evolutionary model on seismic amplification.

(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.

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Supplementary Tables

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 53, 1673–1685 (2021). https://doi.org/10.1038/s41588-021-00951-7

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