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Integrative detection and analysis of structural variation in cancer genomes

Nature Genetics volume 50, pages 1388–1398 (2018)Cite this article

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Abstract

Structural variants (SVs) can contribute to oncogenesis through a variety of mechanisms. Despite their importance, the identification of SVs in cancer genomes remains challenging. Here, we present a framework that integrates optical mapping, high-throughput chromosome conformation capture (Hi-C), and whole-genome sequencing to systematically detect SVs in a variety of normal or cancer samples and cell lines. We identify the unique strengths of each method and demonstrate that only integrative approaches can comprehensively identify SVs in the genome. By combining Hi-C and optical mapping, we resolve complex SVs and phase multiple SV events to a single haplotype. Furthermore, we observe widespread structural variation events affecting the functions of noncoding sequences, including the deletion of distal regulatory sequences, alteration of DNA replication timing, and the creation of novel three-dimensional chromatin structural domains. Our results indicate that noncoding SVs may be underappreciated mutational drivers in cancer genomes.

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Fig. 1: Overall strategy of SV detection in cancer genomes.
Fig. 2: Detection of SVs using Hi-C in cancer genomes.
Fig. 3: Comparison of SVs detected by different methods.
Fig. 4: The impact of SVs on enhancers.
Fig. 5: Rearrangements and TAD fusions.

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Acknowledgements

This work was supported by NIH grants R35GM124820, R01HG009906, and U01CA200060 (F.Y.), R24DK106766 (R.C.H. and F.Y.), GM083337 (D.M.G.), GM085354 (D.M.G.), DK107965 (D.M.G.), U54HG004592 (J.D. and J.A.S.), HG003143 and DK107980 (J.D.), U41HG007000 (W.S.N.), and DP5OD023071 (J. D.). This work was also supported by European Research Council (No. 615584 to D.T.O.and C.E.), Cancer Research UK (Nos. 20412 and 22398 to D.T.O. and C.E.), Wellcome Trust (No. 84459 to D.T.O. and C.E.), and Wellcome Trust (No. 106985/Z/15/Z to S.H.). J.D. is an investigator of the Howard Hughes Medical Institute. J.R.D. is also supported by the Leona M. and Harry B. Helmsley Charitable Trust grant No. 2017-PG-MED001. F.A. was supported by Institute Leadership Funds from La Jolla Institute for Allergy and Immunology. F.Y. is also supported by the Leukemia Research Foundation and Penn State Clinical and Translational Science Institute. We thank the ENCODE Data Coordination Center for helping with Hi-C and replication time data deposition. We would also like to thank Jan Karlseder and Nausica Arnault for help with the FISH experiments.

Author information

Author notes

  1. These authors contributed equally to this work: Jesse R. Dixon, Jie Xu, Vishnu Dileep, Ye Zhan, and Fan Song.

Authors and Affiliations

  1. Salk Institute for Biological Studies, La Jolla, CA, USA

    Jesse R. Dixon & Victoria T. Le

  2. Department of Biochemistry and Molecular Biology, College of Medicine, The Pennsylvania State University, Hershey, PA, USA

    Jie Xu, Lijun Zhang, Hongbo Yang, Tingting Liu, Sriranga Iyyanki, James R. Broach & Feng Yue

  3. Department of Biological Science, Florida State University, Tallahassee, FL, USA

    Vishnu Dileep, Takayo Sasaki, Juan Carlos Rivera-Mulia & David M. Gilbert

  4. Program in Systems Biology, Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, MA, USA

    Ye Zhan, Hakan Ozadam, Bryan R. Lajoie & Job Dekker

  5. Bioinformatics and Genomics Program, The Pennsylvania State University, University Park, State College, PA, USA

    Fan Song, Yanli Wang, Lin An & Feng Yue

  6. Department of Genome Sciences, University of Washington, Seattle, WA, USA

    Galip Gürkan Yardımcı & William Stafford Noble

  7. La Jolla Institute for Allergy and Immunology, La Jolla, CA, USA

    Abhijit Chakraborty & Ferhat Ay

  8. Division of Otolaryngology, Head & Neck Surgery, Milton S. Hershey Medical Center, Hershey, PA, USA

    Darrin V. Bann & Christopher Pool

  9. Penn State College of Medicine, Informatics and Technology, Hershey, PA, USA

    Royden Clark

  10. Altius institute for Biomedical Sciences, Seattle, WA, USA

    Rajinder Kaul, Michael Buckley, Kristen Lee, Morgan Diegel & John A. Stamatoyannopoulos

  11. Research Department of Cancer Biology, Cancer Institute, University College London, London, UK

    Dubravka Pezic & Suzana Hadjur

  12. Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK

    Christina Ernst & Duncan T. Odom

  13. German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics, Heidelberg, Germany

    Duncan T. Odom

  14. Center for Comparative Genomics and Bioinformatics, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, State College, PA, USA

    Ross C. Hardison

  15. School of Medicine, University of California San Diego, La Jolla, CA, USA

    Ferhat Ay

  16. Howard Hughes Medical Institute, Chevy Chase, MD, USA

    Job Dekker

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  1. Jesse R. Dixon
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Contributions

J.X., J.R.D., F.S., and F.Y. led the overall integrative analysis. J.X. and S.F. performed the WGS data analysis. J.R.D. led the overall Hi-C analysis. ENCODE Hi-C data were generated by Y. Z. and analyzed by B.R.L., H.O., and J.D. J.R.D., V.T.L., J.X., and F.Y. performed the additional Hi-C and FISH experiments. J.X., F.Y., A.C. and F.A. contributed to Hi-C analysis. J.X., D.V.B., R.C., J.B., L.Z., C.P., J.R.B., and F.Y. performed the optical mapping and data analysis. V.D., T.S., J.C., and D.G. led the replication timing analysis. C.E. and D.O. prepared the Tc1 material. D.P. and S.H. prepared the Hi-C experiments on Tc1 cells and the preliminary analysis. G.Y., L.Z., H.Y., T.L., S.I., L.A., C.P., R.K., M.B., K.L., M.D., J.S., and D.G. analyzed the data. J.R.D., J.X., V.D., F.S., F.A., R.C.H., W.S.N., J.D., D.G., and F.Y. wrote the manuscript.

Corresponding authors

Correspondence to Jesse R. Dixon, Ferhat Ay, William Stafford Noble, Job Dekker, David M. Gilbert or Feng Yue.

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The authors declare no competing interests.

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Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–24

Reporting Summary

Supplementary Note

Supplementary Table 1

List of cell/tissue types with performed experiments and analysis

Supplementary Table 2

Number of SVs detected by WGS, Hi-C and optical mapping in eight cancer cell lines and NA12878

Supplementary Table 3

SVs detected by WGS in eight cancer cell lines and NA12878

Supplementary Table 4

SVs detected by optical mapping in eight cancer cell lines and NA12878

Supplementary Table 5

SVs detected by Hi-C in 36 cell lines

Supplementary Table 6

High-confidence SV calls from integration

Supplementary Table 7

Validated translocations and deletions in K562, Caki and T47D cells

Supplementary Table 8

Cross comparison of large intrachromosomal rearrangements (≥1 Mb) and interchromosomal translocations

Supplementary Table 9

Contribution by each method and their overlapping percentage with high-confidence SVs

Supplementary Table 10

Integration of intrachromosomal rearrangements (<1 Mb)

Supplementary Table 11

Irys-detected deletions encompass multiple smaller WGS-detected deletions with the same total deletion sizes

Supplementary Table 12

Optical mapping predicts the size of unresolved genome gap in hg19

Supplementary Table 13

Optical mapping provides estimation of gap size in hg38 and comparison to previous gap assessment of hg38

Supplementary Table 14

SV-induced fused genes detected by RNA-seq

Supplementary Table 15

Summary of genes, repetitive elements and insulators overlapping with high-confidence deletions

Supplementary Table 16

Frequency of enhancer deletions versus simulated expectation in cancer cells and normal cells

Supplementary Table 17

Deleted potential enhancers and insulators in T47D, Caki2, K562 and NCIH460

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Dixon, J.R., Xu, J., Dileep, V. et al. Integrative detection and analysis of structural variation in cancer genomes. Nat Genet 50, 1388–1398 (2018). https://doi.org/10.1038/s41588-018-0195-8

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  • DOI: https://doi.org/10.1038/s41588-018-0195-8

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