Tumour evolution inferred by single-cell sequencing (original) (raw)

Nature volume 472, pages 90–94 (2011)Cite this article

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Abstract

Genomic analysis provides insights into the role of copy number variation in disease, but most methods are not designed to resolve mixed populations of cells. In tumours, where genetic heterogeneity is common1,2,3, very important information may be lost that would be useful for reconstructing evolutionary history. Here we show that with flow-sorted nuclei, whole genome amplification and next generation sequencing we can accurately quantify genomic copy number within an individual nucleus. We apply single-nucleus sequencing to investigate tumour population structure and evolution in two human breast cancer cases. Analysis of 100 single cells from a polygenomic tumour revealed three distinct clonal subpopulations that probably represent sequential clonal expansions. Additional analysis of 100 single cells from a monogenomic primary tumour and its liver metastasis indicated that a single clonal expansion formed the primary tumour and seeded the metastasis. In both primary tumours, we also identified an unexpectedly abundant subpopulation of genetically diverse ‘pseudodiploid’ cells that do not travel to the metastatic site. In contrast to gradual models of tumour progression, our data indicate that tumours grow by punctuated clonal expansions with few persistent intermediates.

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Sequence Read Archive

Data deposits

All data has been deposited into the NCBI Sequence Read Archive under accession number SRA018951.105.

References

  1. Park, S. Y., Gonen, M., Kim, H. J., Michor, F. & Polyak, K. Cellular and genetic diversity in the progression of in situ human breast carcinomas to an invasive phenotype. J. Clin. Invest. 120, 636–644 (2010)
    Article CAS Google Scholar
  2. Torres, L. et al. Intratumor genomic heterogeneity in breast cancer with clonal divergence between primary carcinomas and lymph node metastases. Breast Cancer Res. Treat. 102, 143–155 (2007)
    Article Google Scholar
  3. Farabegoli, F. et al. Clone heterogeneity in diploid and aneuploid breast carcinomas as detected by FISH. Cytometry 46, 50–56 (2001)
    Article CAS Google Scholar
  4. Chiang, D. Y. et al. High-resolution mapping of copy-number alterations with massively parallel sequencing. Nature Methods 6, 99–103 (2009)
    Article CAS Google Scholar
  5. Yoon, S., Xuan, Z., Makarov, V., Ye, K. & Sebat, J. Sensitive and accurate detection of copy number variants using read depth of coverage. Genome Res. 19, 1586–1592 (2009)
    Article CAS Google Scholar
  6. Alkan, C. et al. Personalized copy number and segmental duplication maps using next-generation sequencing. Nature Genet. 41, 1061–1067 (2009)
    Article CAS Google Scholar
  7. Geigl, J. B. et al. Identification of small gains and losses in single cells after whole genome amplification on tiling oligo arrays. Nucleic Acids Res. 37, e105 (2009)
    Article Google Scholar
  8. Fuhrmann, C. et al. High-resolution array comparative genomic hybridization of single micrometastatic tumor cells. Nucleic Acids Res. 36, e39 (2008)
    Article Google Scholar
  9. Pugh, T. J. et al. Impact of whole genome amplification on analysis of copy number variants. Nucleic Acids Res. 36, e80 (2008)
    Article CAS Google Scholar
  10. Talseth-Palmer, B. A., Bowden, N. A., Hill, A., Meldrum, C. & Scott, R. J. Whole genome amplification and its impact on CGH array profiles. BMC Res. Notes 1, 56 (2008)
    Article Google Scholar
  11. Hughes, S. et al. Use of whole genome amplification and comparative genomic hybridisation to detect chromosomal copy number alterations in cell line material and tumour tissue. Cytogenet. Genome Res. 105, 18–24 (2004)
    Article CAS Google Scholar
  12. Huang, J., Pang, J., Watanabe, T., Ng, H. K. & Ohgaki, H. Whole genome amplification for array comparative genomic hybridization using DNA extracted from formalin-fixed, paraffin-embedded histological sections. J. Mol. Diagn. 11, 109–116 (2009)
    Article CAS Google Scholar
  13. Navin, N. et al. Inferring tumor progression from genomic heterogeneity. Genome Res. 20, 68–80 (2010)
    Article CAS Google Scholar
  14. Saitou, N. & Nei, M. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4, 406–425 (1987)
    CAS PubMed Google Scholar
  15. Hicks, J. et al. Novel patterns of genome rearrangement and their association with survival in breast cancer. Genome Res. 16, 1465–1479 (2006)
    Article CAS Google Scholar
  16. Prelic, A. et al. A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics 22, 1122–1129 (2006)
    Article CAS Google Scholar
  17. Liu, W. et al. Copy number analysis indicates monoclonal origin of lethal metastatic prostate cancer. Nature Med. 15, 559–565 (2009)
    Article CAS Google Scholar
  18. Ding, L. et al. Genome remodelling in a basal-like breast cancer metastasis and xenograft. Nature 464, 999–1005 (2010)
    Article ADS CAS Google Scholar
  19. Yachida, S. et al. Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature 467, 1114–1117 (2010)
    Article ADS CAS Google Scholar
  20. Nowell, P. C. The clonal evolution of tumor cell populations. Science 194, 23–28 (1976)
    Article ADS CAS Google Scholar
  21. Loeb, L. A., Springgate, C. F. & Battula, N. Errors in DNA replication as a basis of malignant changes. Cancer Res. 34, 2311–2321 (1974)
    CAS PubMed Google Scholar
  22. Bielas, J. H., Loeb, K. R., Rubin, B. P., True, L. D. & Loeb, L. A. Human cancers express a mutator phenotype. Proc. Natl Acad. Sci. USA 103, 18238–18242 (2006)
    Article ADS CAS Google Scholar
  23. Heng, H. H. et al. Stochastic cancer progression driven by non-clonal chromosome aberrations. J. Cell. Physiol. 208, 461–472 (2006)
    Article CAS Google Scholar
  24. Gould, S. J. & Eldredge, N. Punctuated equilibria comes of age. Nature 366, 223–227 (1993)
    Article ADS CAS Google Scholar
  25. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009)
    Article Google Scholar

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Acknowledgements

We thank M. Ronemus, T. Spencer, A. Leotta, J. Meth, M. Kramer, L. Gelley, E. Ghiban. We also thank P. Blake and N. Navin at Sophic Systems Alliance. This work was supported by the NCI T32 Fellowship to N.N., and grants to M.W. and J.H. from the Department of the Army (W81XWH04-1-0477), the Breast Cancer Research Foundation, and the Simons Foundation. M.W. is an American Cancer Society Research Professor.

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Authors and Affiliations

  1. Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, 11724, USA
    Nicholas Navin, Jude Kendall, Jennifer Troge, Peter Andrews, Linda Rodgers, Jeanne McIndoo, Kerry Cook, Asya Stepansky, Dan Levy, Diane Esposito, Alex Krasnitz, W. Richard McCombie, James Hicks & Michael Wigler
  2. Department of Genetics, University of Texas MD Anderson Cancer Center, Houston, 77030, Texas, USA
    Nicholas Navin
  3. Ontario Institute for Cancer Research, Toronto, Ontario M5G 0A3, Canada
    Lakshmi Muthuswamy

Authors

  1. Nicholas Navin
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  2. Jude Kendall
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  3. Jennifer Troge
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  4. Peter Andrews
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  5. Linda Rodgers
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  6. Jeanne McIndoo
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  7. Kerry Cook
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  8. Asya Stepansky
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  9. Dan Levy
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  10. Diane Esposito
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  11. Lakshmi Muthuswamy
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  12. Alex Krasnitz
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  13. W. Richard McCombie
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  14. James Hicks
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  15. Michael Wigler
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Contributions

N.N. designed and performed experiments and analysis, and wrote the manuscript. J.K., A.K., L.M., D.L. and P.A. developed analysis programs. J.T., L.R., K.C., J.M., D.E. and A.S. performed experiments. W.R.M. designed experiments. J.H. and M.W. designed experiments, performed analysis and wrote manuscript.

Corresponding author

Correspondence toMichael Wigler.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Information

This file contains Supplementary Figures 1-8 with legends and Supplementary Methods. (PDF 4792 kb)

Supplementary Table 1

This table shows a summary of 100 Single Cells in the Polygenomic Tumor T10. (XLS 42 kb)

Supplementary Table 2

This table shows a summary of 100 Single Cells in T16P and T16M Metastatic Tumor Pair. (XLS 45 kb)

Supplementary Table 3

This table shows LOH and Copy Number in Tumor Subpopulations. (XLS 28 kb)

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Navin, N., Kendall, J., Troge, J. et al. Tumour evolution inferred by single-cell sequencing.Nature 472, 90–94 (2011). https://doi.org/10.1038/nature09807

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Editorial Summary

Single tumour cells observed

Tumours are known to be genetically heterogeneous, but it is proving difficult to dissect this heterogeneity at the single-cell level. A combination of whole-genome amplification and sequencing of single nuclei separated by fluorescence activated cell sorting now reveals the population structure of breast tumours from two patients. In both, tumour growth is by punctuated clonal expansions with few persistent intermediates, in contrast to the many gradual models of tumour progression. Single-cell sequencing of this type — once it becomes cheaper — is likely to have clinical implications for cancer prognosis and staging.

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