Article | Published:

Punctuated copy number evolution and clonal stasis in triple-negative breast cancer

Nature Genetics volume 48, pages 11191130 (2016) | Download Citation

Abstract

Aneuploidy is a hallmark of breast cancer; however, knowledge of how these complex genomic rearrangements evolve during tumorigenesis is limited. In this study, we developed a highly multiplexed single-nucleus sequencing method to investigate copy number evolution in patients with triple-negative breast cancer. We sequenced 1,000 single cells from tumors in 12 patients and identified 1–3 major clonal subpopulations in each tumor that shared a common evolutionary lineage. For each tumor, we also identified a minor subpopulation of non-clonal cells that were classified as metastable, pseudodiploid or chromazemic. Phylogenetic analysis and mathematical modeling suggest that these data are unlikely to be explained by the gradual accumulation of copy number events over time. In contrast, our data challenge the paradigm of gradual evolution, showing that the majority of copy number aberrations are acquired at the earliest stages of tumor evolution, in short punctuated bursts, followed by stable clonal expansions that form the tumor mass.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Accessions

Primary accessions

Sequence Read Archive

References

  1. 1.

    & Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

  2. 2.

    et al. Novel patterns of genome rearrangement and their association with survival in breast cancer. Genome Res. 16, 1465–1479 (2006).

  3. 3.

    Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).

  4. 4.

    & A genetic model for colorectal tumorigenesis. Cell 61, 759–767 (1990).

  5. 5.

    , , , & Multivariate analysis of chromosomal imbalances in breast cancer delineates cytogenetic pathways and reveals complex relationships among imbalances. Cancer Res. 62, 2675–2680 (2002).

  6. 6.

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

  7. 7.

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

  8. 8.

    et al. Inferring tumor progression from genomic heterogeneity. Genome Res. 20, 68–80 (2010).

  9. 9.

    & Tracing the tumor lineage. Mol. Oncol. 4, 267–283 (2010).

  10. 10.

    et al. Genome-wide copy number analysis of single cells. Nat. Protoc. 7, 1024–1041 (2012).

  11. 11.

    et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011).

  12. 12.

    , & Basal-like breast cancer: a critical review. J. Clin. Oncol. 26, 2568–2581 (2008).

  13. 13.

    , & Triple-negative breast cancer. N. Engl. J. Med. 363, 1938–1948 (2010).

  14. 14.

    et al. Integrative molecular profiling of triple negative breast cancers identifies amplicon drivers and potential therapeutic targets. Oncogene 29, 2013–2023 (2010).

  15. 15.

    et al. The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature 486, 395–399 (2012).

  16. 16.

    et al. Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity. Cell Reports 6, 514–527 (2014).

  17. 17.

    et al. Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature 512, 155–160 (2014).

  18. 18.

    et al. Subclonal diversification of primary breast cancer revealed by multiregion sequencing. Nat. Med. 21, 751–759 (2015).

  19. 19.

    , , & Clustering rules: comparison of partitioning and hierarchical clustering algorithms. J. Math. Model. Algorithms 5, 475–504 (2006).

  20. 20.

    et al. Copynumber: efficient algorithms for single- and multi-track copy number segmentation. BMC Genomics 13, 591 (2012).

  21. 21.

    et al. An evolutionary approach for identifying driver mutations in colorectal cancer. PLoS Comput. Biol. 11, e1004350 (2015).

  22. 22.

    , & Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics 131, 479–491 (1992).

  23. 23.

    et al. Next generation sequencing of triple negative breast cancer to find predictors for chemotherapy response. Breast Cancer Res. 17, 134 (2015).

  24. 24.

    , , & An intuitive graphical visualization technique for the interrogation of transcriptome data. Nucleic Acids Res. 39, 7380–7389 (2011).

  25. 25.

    , , , & Cellular and genetic diversity in the progression of in situ human breast carcinomas to an invasive phenotype. J. Clin. Invest. 120, 636–644 (2010).

  26. 26.

    et al. A Big Bang model of human colorectal tumor growth. Nat. Genet. 47, 209–216 (2015).

  27. 27.

    et al. Extremely high genetic diversity in a single tumor points to prevalence of non-Darwinian cell evolution. Proc. Natl. Acad. Sci. USA 112, E6496–E6505 (2015).

  28. 28.

    , , , & Identification of neutral tumor evolution across cancer types. Nat. Genet. 48, 238–244 (2016).

  29. 29.

    & Punctuated equilibrium comes of age. Nature 366, 223–227 (1993).

  30. 30.

    & in Models in Paleobiology (ed. Schopf, T.J.M.) 82–115 (Freeman, Cooper and Co., 1972).

  31. 31.

    & Cancer chromosomes in crisis. Nat. Genet. 36, 932–934 (2004).

  32. 32.

    , , & Single cell sequencing reveals low levels of aneuploidy across mammalian tissues. Proc. Natl. Acad. Sci. USA 111, 13409–13414 (2014).

  33. 33.

    & Chromosomal instability and cancer: a complex relationship with therapeutic potential. J. Clin. Invest. 122, 1138–1143 (2012).

  34. 34.

    et al. p53 deficiency rescues the adverse effects of telomere loss and cooperates with telomere dysfunction to accelerate carcinogenesis. Cell 97, 527–538 (1999).

  35. 35.

    & A critical role for telomeres in suppressing and facilitating carcinogenesis. Curr. Opin. Genet. Dev. 10, 39–46 (2000).

  36. 36.

    et al. In situ analyses of genome instability in breast cancer. Nat. Genet. 36, 984–988 (2004).

  37. 37.

    et al. Proteins required for centrosome clustering in cancer cells. Sci. Transl. Med. 2, 33ra38 (2010).

  38. 38.

    et al. Mechanisms to suppress multipolar divisions in cancer cells with extra centrosomes. Genes Dev. 22, 2189–2203 (2008).

  39. 39.

    & Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

  40. 40.

    et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

  41. 41.

    , , & Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 5, 557–572 (2004).

  42. 42.

    & A comparison study: applying segmentation to array CGH data for downstream analyses. Bioinformatics 21, 4084–4091 (2005).

  43. 43.

    et al. Interactive analysis and assessment of single-cell copy-number variations. Nat. Methods 12, 1058–1060 (2015).

  44. 44.

    et al. COSMIC: exploring the world's knowledge of somatic mutations in human cancer. Nucleic Acids Res. 43, D805–D811 (2015).

  45. 45.

    & BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

  46. 46.

    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2016).

  47. 47.

    & A dendrite method for cluster analysis. Commun. Stat. 3, 27 (1974).

  48. 48.

    & Finding Groups in Data: An Introduction to Cluster Analysis (Wiley, 2005).

  49. 49.

    & Visualizing high-dimensional data using t-SNE. J. Mach. Learn. Res. 9, 27 (2008).

  50. 50.

    A stagewise rejective multiple test procedure based on a modified Bonferroni test. Biometrika 75, 383–386 (1988).

  51. 51.

    , & Circular Statistics in R (Oxford University Press, 2013).

  52. 52.

    phangorn: phylogenetic analysis in R. Bioinformatics 27, 592–593 (2011).

Download references

Acknowledgements

We thank M. Edgerton, J. Kendall, M. Wigler and J. Hicks for their support and discussions. We are also very grateful to the patients with breast cancer at M.D. Anderson for generously donating their tumor tissues to our research studies. This work was supported by a grant from the Lefkofsky Family Foundation. N.E.N. is a Nadia's Gift Foundation Damon Runyon-Rachleff Innovator (DRR-25-13). This work is also supported by grants to N.E.N. from the NCI (1RO1CA169244-01) and the American Cancer Society (129098-RSG-16-092-01-TBG). N.E.N. is a T.C. Hsu Endowed Scholar, an AAAS Wachtel Scholar and an Andrew Sabin Family Fellow. The study is also supported by the Moonshot Knowledge Gap Award and the Center for Genetics and Genomics. This study was supported by the M.D. Anderson Sequencing Core Facility grant (CA016672) and the Flow Cytometry Facility grant (CA016672) from the NIH. Additional funding support includes the Rosalie B. Hite Fellowship (A.C.); a Center for Genetics and Genomics Postdoctoral Fellowship (R.G.); NIH UL1TR000371 (F.M.-B.); the Nellie B. Connally Breast Cancer Research Endowment (F.M.-B.), Susan Komen SAC10006 (F.M.-B.), CPRIT RP110584 (F.M.-B.) and the M.D. Anderson Cancer Center Support grant (NIH/NCI P30CA016672). F.M. gratefully acknowledges support from the Dana-Farber Cancer Institute Physical Science Oncology Center (U54CA193461-01).

Author information

Affiliations

  1. Department of Genetics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

    • Ruli Gao
    • , Alexander Davis
    • , Emi Sei
    • , Yong Wang
    • , Pei-Ching Tsai
    • , Anna Casasent
    • , Jill Waters
    •  & Nicholas E Navin
  2. Graduate School of Biomedical Sciences, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

    • Alexander Davis
    • , Anna Casasent
    •  & Nicholas E Navin
  3. Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.

    • Thomas O McDonald
    •  & Franziska Michor
  4. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.

    • Thomas O McDonald
    •  & Franziska Michor
  5. Peking Union Medical College, Department of Medical Oncology, Cancer Hospital and Institute, Chinese Academy of Medical Sciences, Beijing, China.

    • Xiuqing Shi
  6. Department of Pathology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

    • Hong Zhang
  7. Department of Surgical Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

    • Funda Meric-Bernstam
  8. Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

    • Nicholas E Navin

Authors

  1. Search for Ruli Gao in:

  2. Search for Alexander Davis in:

  3. Search for Thomas O McDonald in:

  4. Search for Emi Sei in:

  5. Search for Xiuqing Shi in:

  6. Search for Yong Wang in:

  7. Search for Pei-Ching Tsai in:

  8. Search for Anna Casasent in:

  9. Search for Jill Waters in:

  10. Search for Hong Zhang in:

  11. Search for Funda Meric-Bernstam in:

  12. Search for Franziska Michor in:

  13. Search for Nicholas E Navin in:

Contributions

R.G. analyzed the data and wrote the manuscript. A.D. analyzed the data. T.O.M. and F.M. performed mathematical modeling and wrote the manuscript. E.S., X.S., P.-C.T. and J.W. performed experiments. A.C. and Y.W. analyzed the data. H.Z. and F.M.-B. provided tumor samples and interpreted the data. N.E.N. analyzed the data, led the project and wrote the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Nicholas E Navin.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–9, Supplementary Tables 1–6 and Supplementary Note.

Zip files

  1. 1.

    Supplementary Code

    Software code.

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/ng.3641

Further reading