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Starfish infers signatures of complex genomic rearrangements across human cancers

Nature Cancer volume 3, pages 1247–1259 (2022)Cite this article

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Abstract

Complex genomic rearrangements (CGRs) are common in cancer and are known to form via two aberrant cellular structures—micronuclei and chromatin bridges. However, which of these mechanisms is more relevant to CGR formation in cancer and whether there are other undiscovered mechanisms remain unknown. Here we developed a computational algorithm, ‘Starfish’, to analyze 2,014 CGRs from 2,428 whole-genome-sequenced (WGS) tumors and discovered six CGR signatures based on their copy number and breakpoint patterns. Extensive benchmarking showed that our CGR signatures are highly accurate and biologically meaningful. Three signatures can be attributed to known biological processes—micronuclei- and chromatin-bridge-induced chromothripsis and circular extrachromosomal DNA. Over half of the CGRs belong to the remaining three signatures, not reported previously. A unique signature, which we named ‘hourglass chromothripsis’, with localized breakpoints and a low amount of DNA loss, is abundant in prostate cancer. Hourglass chromothripsis is associated with mutant SPOP, which may induce genome instability.

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Fig. 1: Six CGR signatures detected in the PCAWG cohort.
Fig. 2: Benchmarking CGR signatures.
Fig. 3: Distribution of CGRs.
Fig. 4: Genetic associations of CGRs.
Fig. 5: Biases of CGR breakpoints.
Fig. 6: Reconstruction of hourglass chromothripsis using linked-read sequencing data.
Fig. 7: Hourglass chromothripsis in prostate cancer.

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Data availability

Raw sequencing data for 329 ICGC prostate cancers are available at European Genome-phenome Archive with the accession codes EGAS00001000900 and EGAS00001000262. Linked-read WGS on 23 prostate cancers were obtained from dbGAP with the identifier phs001577.v1.p1. Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

Code availability

The Starfish package is available at https://github.com/yanglab-computationalgenomics/Starfish.

References

  1. Negrini, S., Gorgoulis, V. G. & Halazonetis, T. D. Genomic instability—an evolving hallmark of cancer. Nat. Rev. Mol. Cell Biol. 11, 220–228 (2010).

    Article  CAS  PubMed  Google Scholar 

  2. Yang, L. et al. Diverse mechanisms of somatic structural variations in human cancer genomes. Cell 153, 919–929 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Campbell, P. J. et al. Pan-cancer analysis of whole genomes. Nature 578, 82–93 (2020).

    Google Scholar 

  4. Conrad, D. F. et al. Origins and functional impact of copy number variation in the human genome. Nature 464, 704–712 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Stephens, P. J. et al. Massive genomic rearrangement acquired in a single catastrophic event during cancer development. Cell 144, 27–40 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Cortés-Ciriano, I. et al. Comprehensive analysis of chromothripsis in 2,658 human cancers using whole-genome sequencing. Nat. Genet. 52, 331–341 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Liu, P. et al. Chromosome catastrophes involve replication mechanisms generating complex genomic rearrangements. Cell 146, 889–903 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Holland, A. J. & Cleveland, D. W. Chromoanagenesis and cancer: mechanisms and consequences of localized, complex chromosomal rearrangements. Nat. Med. 18, 1630–1638 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Baca, S. C. et al. Punctuated evolution of prostate cancer genomes. Cell 153, 666–677 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Giardiello, F. M. et al. Guidelines on genetic evaluation and management of lynch syndrome: a consensus statement by the US Multi-Society Task Force on Colorectal Cancer. Gastroenterology 147, 502–526 (2014).

    Article  PubMed  Google Scholar 

  11. Fong, P. C. et al. Inhibition of poly(ADP-ribose) polymerase in tumors from BRCA mutation carriers. N. Engl. J. Med. 361, 123–134 (2009).

    Article  CAS  PubMed  Google Scholar 

  12. Alexandrov, L. B. et al. Deciphering signatures of mutational processes operative in human cancer. Cell Rep. 3, 246–259 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Macintyre, G. et al. Copy number signatures and mutational processes in ovarian carcinoma. Nat. Genet. 50, 1262–1270 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Chiang, C. et al. The impact of structural variation on human gene expression. Nat. Genet. 49, 692–699 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Zhang, C.-Z. et al. Chromothripsis from DNA damage in micronuclei. Nature 522, 179–184 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Ly, P. et al. Selective Y centromere inactivation triggers chromosome shattering in micronuclei and repair by non-homologous end joining. Nat. Cell Biol. 19, 68–75 (2017).

    Article  CAS  PubMed  Google Scholar 

  17. Maciejowski, J., Li, Y., Bosco, N., Campbell, P. J. & de Lange, T. Chromothripsis and kataegis induced by telomere crisis. Cell 163, 1641–1654 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Maciejowski, J. et al. APOBEC3-dependent kataegis and TREX1-driven chromothripsis during telomere crisis. Nat. Genet. 52, 884–890 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Umbreit, N. T. et al. Mechanisms generating cancer genome complexity from a single cell division error. Science 368, eaba0712 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Shoshani, O. et al. Chromothripsis drives the evolution of gene amplification in cancer. Nature 591, 137–141 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kim, H. et al. Extrachromosomal DNA is associated with oncogene amplification and poor outcome across multiple cancers. Nat. Genet. 52, 891–897 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Hadi, K. et al. Distinct classes of complex structural variation uncovered across thousands of cancer genome graphs. Cell 183, 197–210 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Korbel, J. O. & Campbell, P. J. Criteria for inference of chromothripsis in cancer genomes. Cell 152, 1226–1236 (2013).

    Article  CAS  PubMed  Google Scholar 

  25. Li, Y. et al. Constitutional and somatic rearrangement of chromosome 21 in acute lymphoblastic leukaemia. Nature 508, 98–102 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Leibowitz, M. L. et al. Chromothripsis as an on-target consequence of CRISPR–Cas9 genome editing. Nat. Genet. 53, 895–905 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Shiwei, L. et al. Nuclear envelope assembly defects link mitotic errors to chromothripsis. Nature 561, 551–555 (2018).

    Article  Google Scholar 

  28. Peter, L. et al. Chromosome segregation errors generate a diverse spectrum of simple and complex genomic rearrangements. Nat. Genet. 51, 705–715 (2019).

    Article  Google Scholar 

  29. Mitchell, T. J. et al. Timing the landmark events in the evolution of clear cell renal cell cancer: TRACERx Renal. Cell 173, 611–623 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Yang, L. et al. Analyzing somatic genome rearrangements in human cancers by using whole-exome sequencing. Am. J. Hum. Genet. 98, 843–856 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Raphael, B. J. et al. Integrated genomic characterization of pancreatic ductal adenocarcinoma. Cancer Cell 32, 185–203 (2017).

    Article  Google Scholar 

  32. Chan-Seng-Yue, M. et al. Transcription phenotypes of pancreatic cancer are driven by genomic events during tumor evolution. Nat. Genet. 52, 231–240 (2020).

    Article  CAS  PubMed  Google Scholar 

  33. Rausch, T. et al. Genome sequencing of pediatric medulloblastoma links catastrophic DNA rearrangements with TP53 mutations. Cell 148, 59–71 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Yang, L. et al. An enhanced genetic model of colorectal cancer progression history. Genome Biol. 20, 168 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Bergstrom, E. N. et al. Mapping clustered mutations in cancer reveals APOBEC3 mutagenesis of ecDNA. Nature 602, 510–517 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Ganem, N. J., Godinho, S. A. & Pellman, D. A mechanism linking extra centrosomes to chromosomal instability. Nature 460, 278–282 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Dewhurst, S. M. et al. Tolerance of whole-genome doubling propagates chromosomal instability and accelerates cancer genome evolution. Cancer Discov. 4, 175–185 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Li, Y. et al. Patterns of somatic structural variation in human cancer genomes. Nature 578, 112–121 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Macheret, M. & Halazonetis, T. D. DNA replication stress as a hallmark of cancer. Annu. Rev. Pathol. Mech. Dis. 10, 425–448 (2015).

    Article  CAS  Google Scholar 

  40. García-Muse, T. & Aguilera, A. Transcription-replication conflicts: how they occur and how they are resolved. Nat. Rev. Mol. Cell Biol. 17, 553–563 (2016).

    Article  PubMed  Google Scholar 

  41. Helmrich, A., Ballarino, M. & Tora, L. Collisions between replication and transcription complexes cause common fragile site instability at the longest human genes. Mol. Cell 44, 966–977 (2011).

    Article  CAS  PubMed  Google Scholar 

  42. Sankar, T. S., Wastuwidyaningtyas, B. D., Dong, Y., Lewis, S. A. & Wang, J. D. The nature of mutations induced by replication-transcription collisions. Nature 535, 178–181 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Haradhvala, N. J. et al. Mutational strand asymmetries in cancer genomes reveal mechanisms of DNA damage and repair. Cell 164, 538–549 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Carvalho, C. M. B. et al. Replicative mechanisms for CNV formation are error prone. Nat. Genet. 45, 1319–1327 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Viswanathan, S. R. et al. Structural alterations driving castration-resistant prostate cancer revealed by linked-read genome sequencing. Cell 174, 433–447 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Fraser, M. et al. Genomic hallmarks of localized, non-indolent prostate cancer. Nature 541, 359–364 (2017).

    Article  CAS  PubMed  Google Scholar 

  47. Wedge, D. C. et al. Sequencing of prostate cancers identifies new cancer genes, routes of progression and drug targets. Nat. Genet. 50, 682–692 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Abeshouse, A. et al. The molecular taxonomy of primary prostate cancer. Cell 163, 1011–1025 (2015).

    Article  CAS  Google Scholar 

  49. Tomlins, S. A. et al. Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. Science 310, 644–648 (2005).

    Article  CAS  PubMed  Google Scholar 

  50. Dewhurst., S. M. et al. Structural variant evolution after telomere crisis. Nat. Commun. 12, 2093 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  53. Yoshida, W. et al. Identification of G-quadruplex clusters by high-throughput sequencing of whole-genome amplified products with a G-quadruplex ligand. Sci. Rep. 8, 3116 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Kumar, R. et al. HumCFS: a database of fragile sites in human chromosomes. BMC Genomics 19, 985 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Guelen, L. et al. Domain organization of human chromosomes revealed by mapping of nuclear lamina interactions. Nature 453, 948–951 (2008).

    Article  CAS  PubMed  Google Scholar 

  56. Canisius, S., Martens, J. W. M. & Wessels, L. F. A. A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains most co-occurrence. Genome Biol. 17, 261 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Sondka, Z. et al. The COSMIC Cancer Gene Census: describing genetic dysfunction across all human cancers. Nat. Rev. Cancer 18, 696–705 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Mermel, C. H. et al. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 12, R41 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Greer, S. U. & Ji, H. P. Structural variant analysis for linked-read sequencing data with gemtools. Bioinformatics 35, 4397–4399 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the Center for Research Informatics at the University of Chicago for providing the computing infrastructure, and M. Stephens, X. He and M. Le Beau for helpful suggestions. The work was supported by the National Institutes of Health (grant no. K22CA193848 to L.Y.) and the University of Chicago and UChicago Comprehensive Cancer Center (L.Y.).

Author information

Author notes

  1. Lisui Bao

    Present address: Institute of Evolution & Marine Biodiversity, Ocean University of China, Qingdao, China

Authors and Affiliations

  1. Ben May Department for Cancer Research, University of Chicago, Chicago, IL, USA

    Lisui Bao, Xiaoming Zhong, Yang Yang & Lixing Yang

  2. Department of Human Genetics, University of Chicago, Chicago, IL, USA

    Lixing Yang

  3. University of Chicago Comprehensive Cancer Center, Chicago, IL, USA

    Lixing Yang

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Contributions

L.B. and L.Y. conceived the study idea and developed the methodology. L.B., X.Z., Y.Y. and L.Y. performed data analysis. L.Y. supervised the work and wrote the paper.

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Correspondence to Lixing Yang.

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Nature Cancer thanks Paul Mischel 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 Modification of Shatterseek by removing oscillating-copy-number-state requirement.

a, Examples of CGRs not detected by the original version of ShatterSeek. The SV and copy number profiles are shown for four CGRs. CGR regions are marked by red bars below SVs. b, Comparisons between CGR seed regions detected with and without oscillating-copy-state requirement. Breakpoint enrichment test is a binomial test corrected for mappability to evaluate the enrichment of SVs in each chromosome. Exponential distribution test evaluates whether the distribution of SV breakpoints differ from an exponential distribution. The smaller P values for breakpoint enrichment test and exponential test the better. Fragment joins test evaluates whether the distribution of DNA fragment joins diverges from a multinomial distribution with equal probabilities for each category using the goodness-of-fit test for the multinomial distribution. The larger P values for fragment joins test the better. FDR correction was performed on all P values. The newly detected CGRs without oscillating-copy-state have better P values for exponential distribution test and comparable P values for fragment joins test compared to CGRs detected by the original Shatterseek. CGRs detected with oscillating-copy-state have better P values in breakpoint enrichment test because more CGRs of Signatures 1 and 2 are detected with oscillating-copy-state (Figure S4c) and these CGRs have more SVs (Fig. 2b). Although the newly detected CGRs are less enriched in each chromosome, they all pass the Shatterseek P value cutoff.

Source data

Extended Data Fig. 2 Clustering of CGRs.

a, Correlation heatmap of twelve genomic features. The colors and numbers represent the correlation coefficients. b, Distributions of seven genomic features with low correlations. The x axes are normalized Z scores. Two features (median breakpoint microhomology size and median breakpoint insertion size) have low variations and are not used in the clustering step. c, Four indexes to evaluate number of clusters. d, Unsupervised clusters produced by three clustering methods (K-means, Hierarchical clustering with Euclidean distance and Hierarchical clustering with Pearson distance).

Source data

Extended Data Fig. 3 Starfish classifier and benchmarking CGR signatures.

a, A neural network classifier (Starfish classifier) to classify any given CGRs into one of the six signatures derived from the PCAWG cohort. The left panel shows the scheme of neural network classifier, and the right panel shows performance of the neural network classifier. b, Comparison between ecDNA predicted by AmpliconArchitect, tyfonas events predicted by JaBbA, and CGR Signature 1. c, CGRs classified by AmpliconArchitect and JaBbA in five experimental studies. The raw sequencing data, which is required by AmpliconArchitect, are not available for several samples. Therefore, the number of CGRs classified by AmpliconArchitect is less than that of Starfish and JaBbA. d, Fractions of foldback inversions in six CGR signatures.

Source data

Extended Data Fig. 4 Performances of other clustering approaches.

a, Seven clusters formed using five features. Signature 1 splits into two clusters (1a and 1b). b, Comparisons of Signatures 1a and 1b to ecDNA detected by AmpliconArchitect and HSRs detected by JaBbA. c, Proportions CGRs detected with and without oscillating-copy-state requirement. d, Benchmarking CGR classification if unmodified Shatterseek is used to detect CGRs.

Source data

Extended Data Fig. 5 Performances of clustering using different features.

a, Benchmarking CGR classification by replacing one feature from the five used in Fig. 1b. b, Benchmarking CGR classification by adding one feature. c, Six clusters formed using ten features. All features related to CGR CNV and SV properties are used. The SV breakpoint microhomology and insertion size are not used because these measures are not available in the experimentally induced CGRs. d, Benchmarking CGR classification by using ten features. In each benchmarking test in a, b and d, CGRs from five experimental studies are used. Each colored bar shows CGRs classified with the corresponding features in each study. The numbers of corrected classified CGRs and total CGRs are displayed above the bars.

Source data

Extended Data Fig. 6 Distribution of CGRs.

a, CGR frequencies in tumor types with less than 20 samples. Tumors are painted by CGR signatures. If one tumor carries more than one CGR signatures, it is painted by more than one colors horizontally. The height of each tumor may be different in different tumor types since all tumor types are scaled to the same height. b, Occurrences of CGRs in four breast cancer subtypes. c, Frequencies of CGRs per chromosome in different tumor types. CGRs from chromosomes 1 to X are shown with 23 bars and painted by their signatures. The numbers after tumor types denote sample sizes. d, CGR breakpoint hotspots and cancer-driving genes. CGR breakpoint frequencies on most frequent chromosomes for 18 tumor types. Each vertical line represents the number of tumors having CGR breakpoints in a 100 kb or a 1 Mb window.

Source data

Extended Data Fig. 7 Kataegis and WGD co-occuring with CGRs.

a, Numbers of CGRs with and without co-occurring kataegis in each tumor type stratified by five CGR signatures. Signature 1 is shown in Fig. 3d. b, Percentages of tumors with and without WGD stratified by TP53 mutation status, tumor type and CGR signature. Only tumor types with at least five samples in no CGR group and at least five samples in any one of the CGR signature group are displayed. P values calculated by two-sided Fisher’s exact test with Bonferroni correction are shown above the CGR signatures with at least five samples. The ones significant at 0.05 level are labelled in red.

Source data

Extended Data Fig. 8 Transcription-replication collision and CGR breakpoints.

a, Defining DNA replication orientations based on replication-timing profile of Bg02es cell line. b, Replication timing profiles and replication orientations in four other cell lines (Bj, HepG2, HelaS3 and MCF7). c, Breakpoint biases in six CGR signatures stratified by gene expression level from normal tissues. d, CGR breakpoint biases after excluding breakpoints within 1 Mb of CGR hotspots. e, CGR breakpoint biases computed using conserved left- and right-replicated regions identified from six cell lines. In c, d and e, P values are calculated by comparing observed breakpoints and randomly shuffled breakpoints in head-on and co-directional collision regions using two-sided Chi-square tests. Bonferroni corrections are performed. Dashed lines represent the 0.05 P value cutoff.

Source data

Extended Data Fig. 9 Hourglass chromothripsis in prostate cancer.

a, Six CGR signatures compared to ChainFinder-predicted and junction-pattern-predicted chromoplexy events. b, Somatic mutation distribution in SPOP gene in prostate cancer. All mutations are missense mutations in the MATH/TRAF domain which is the target binding domain. c, Number of simple SVs in prostate cancers with and without SPOP mutations. P value is calculated by two-sided Wilcoxon rank sum test.

Source data

Extended Data Fig. 10 Identification of CGR seed and linked regions.

Genomic regions satisfying interleaved SVs, goodness-of-fit, fragment joins test, chromosomal enrichment test, and exponential distribution of breakpoints test using the ShatterSeek package are defined as CGR seed regions. Linked regions are defined as regions connected to seed regions by at least two translocations. All seed and linked regions combined are defined as one CGR event.

Source data

Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Table 1. Coordinates and signatures of 2,014 identified CGRs from PCAWG samples. Supplementary Table 2. Benchmarking Starfish, AmpliconArchitect and JaBbA by CGRs with known mechanisms. Supplementary Table 3. Genetic associations of CGRs in individual tumor types. Supplementary Table 4. Coordinates and signatures of 359 CGRs from combined cohort of 516 prostate cancers.

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Bao, L., Zhong, X., Yang, Y. et al. Starfish infers signatures of complex genomic rearrangements across human cancers. Nat Cancer 3, 1247–1259 (2022). https://doi.org/10.1038/s43018-022-00404-y

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