Article | Published:

Clonal evolution in breast cancer revealed by single nucleus genome sequencing

Nature volume 512, pages 155160 (14 August 2014) | Download Citation

Subjects

Abstract

Sequencing studies of breast tumour cohorts have identified many prevalent mutations, but provide limited insight into the genomic diversity within tumours. Here we developed a whole-genome and exome single cell sequencing approach called nuc-seq that uses G2/M nuclei to achieve 91% mean coverage breadth. We applied this method to sequence single normal and tumour nuclei from an oestrogen-receptor-positive (ER+) breast cancer and a triple-negative ductal carcinoma. In parallel, we performed single nuclei copy number profiling. Our data show that aneuploid rearrangements occurred early in tumour evolution and remained highly stable as the tumour masses clonally expanded. In contrast, point mutations evolved gradually, generating extensive clonal diversity. Using targeted single-molecule sequencing, many of the diverse mutations were shown to occur at low frequencies (<10%) in the tumour mass. Using mathematical modelling we found that the triple-negative tumour cells had an increased mutation rate (13.3×), whereas the ER+ tumour cells did not. These findings have important implications for the diagnosis, therapeutic treatment and evolution of chemoresistance in breast cancer.

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.

    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)

  2. 2.

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

  3. 3.

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

  4. 4.

    et al. Gene expression patterns of carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl Acad. Sci. USA 98, 10869–10874 (2001)

  5. 5.

    et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346–352 (2012)

  6. 6.

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

  7. 7.

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

  8. 8.

    et al. The life history of 21 breast cancers. Cell 149, 994–1007 (2012)

  9. 9.

    et al. Whole-genome analysis informs breast cancer response to aromatase inhibition. Nature 486, 353–360 (2012)

  10. 10.

    et al. Detection of ultra-rare mutations by next-generation sequencing. Proc. Natl Acad. Sci. USA 109, 14508–14513 (2012)

  11. 11.

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

  12. 12.

    et al. One bacterial cell, one complete genome. PLoS ONE 5, e10314 (2010)

  13. 13.

    et al. Artificial polyploidy improves bacterial single cell genome recovery. PLoS ONE 7, e37387 (2012)

  14. 14.

    et al. Single-cell exome sequencing and monoclonal evolution of a JAK2-negative myeloproliferative neoplasm. Cell 148, 873–885 (2012)

  15. 15.

    et al. Comparative genomic hybridization, loss of heterozygosity, and DNA sequence analysis of single cells. Proc. Natl Acad. Sci. USA 96, 4494–4499 (1999)

  16. 16.

    et al. Rapid, low-input, low-bias construction of shotgun fragment libraries by high-density in vitro transposition. Genome Biol. 11, R119 (2010)

  17. 17.

    et al. Chromosomal alterations in 15 breast cancer cell lines by comparative genomic hybridization and spectral karyotyping. Genes Chromosomes Cancer 28, 308–317 (2000)

  18. 18.

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

  19. 19.

    , , & Genome-wide detection of single-nucleotide and copy-number variations of a single human cell. Science 338, 1622–1626 (2012)

  20. 20.

    Methods of measuring the concentration of wealth. J. Am. Stat. Assoc. 9, 209–219 (1905)

  21. 21.

    et al. A method and server for predicting damaging missense mutations. Nature Methods 7, 248–249 (2010)

  22. 22.

    & SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res. 31, 3812–3814 (2003)

  23. 23.

    et al. Tumor growth rate and prognosis of breast cancer mainly detected by mass screening. Jpn. J. Cancer Res. 81, 454–462 (1990)

  24. 24.

    , , , & Age-dependent growth rate of primary breast cancer. Cancer 71, 3547–3551 (1993)

  25. 25.

    et al. Estimates of breast cancer growth rate and sojourn time from screening database information. J. Women’s Imaging 5, 11–19 (2003)

  26. 26.

    & Estimate of the mutation rate per nucleotide in humans. Genetics 156, 297–304 (2000)

  27. 27.

    , , & Rates of spontaneous mutation. Genetics 148, 1667–1686 (1998)

  28. 28.

    , & DNA replication fidelity and cancer. Semin. Cancer Biol. 20, 281–293 (2010)

  29. 29.

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

  30. 30.

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

  31. 31.

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

  32. 32.

    Cell biology: aneuploidy and cancer. Nature 446, 38–39 (2007)

  33. 33.

    The stability of broken ends of chromosomes in Zea mays. Genetics 26, 234–282 (1941)

  34. 34.

    Human cancers express mutator phenotypes: origin, consequences and targeting. Nature Rev. Cancer 11, 450–457 (2011)

  35. 35.

    , , & Cancer as an evolutionary and ecological process. Nature Rev. Cancer 6, 924–935 (2006)

  36. 36.

    & Clonal evolution in cancer. Nature 481, 306–313 (2012)

  37. 37.

    & Mutations of bacteria from virus sensitivity to virus resistance. Genetics 28, 491–511 (1943)

  38. 38.

    , , , & Human cancers express a mutator phenotype. Proc. Natl Acad. Sci. USA 103, 18238–18242 (2006)

  39. 39.

    et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013)

  40. 40.

    et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013)

  41. 41.

    et al. Mutational landscape and significance across 12 major cancer types. Nature 502, 333–339 (2013)

  42. 42.

    & Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009)

  43. 43.

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

  44. 44.

    et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010)

  45. 45.

    et al. CREST maps somatic structural variation in cancer genomes with base-pair resolution. Nature Methods 8, 652–654 (2011)

  46. 46.

    et al. A census of human cancer genes. Nature Rev. Cancer 4, 177–183 (2004)

  47. 47.

    et al. The UCSC known genes. Bioinformatics 22, 1036–1046 (2006)

  48. 48.

    et al. Novel genomic alterations and clonal evolution in chronic lymphocytic leukemia revealed by representational oligonucleotide microarray analysis (ROMA). Blood 113, 1294–1303 (2009)

  49. 49.

    , & ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010)

  50. 50.

    et al. COSMIC: mining complete cancer genomes in the catalogue of somatic mutations in cancer. Nucleic Acids Res. 39, D945–D950 (2011)

  51. 51.

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

  52. 52.

    & The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4, 406–425 (1987)

Download references

Acknowledgements

We thank L. Ramagli, H. Tang, E. Thompson, K. Khanna, W. Schober and J. Tyler. We are grateful to S. Kennedy and L. Loeb for help with the duplex protocols. We thank M. Edgerton, J. Hicks, M. Wigler and J. Kendall for discussions. We thank R. Krahe and M. Rui for reviewing the manuscript. N.E.N. is a Nadia’s Gift Foundation Damon Runyon-Rachleff Innovator (DRR-25-13). This research was supported by grants to N.E.N. from NIH (R21CA174397-01) and NCI (1RO1CA169244-01). N.E.N. was supported by T.C. Hsu and the Alice-Reynolds Kleberg Foundation. N.E.N. and P.S. were supported by the Center for Genetics & Genomics. F.M.-B was supported by an NIH UL1 (TR000371) and Susan Komen (SAC10006). K.C. was supported by the NCI (RO1CA172652). H.L. was supported by the NIH (U24CA143883). F.M. was supported by PS-OC (U54CA143798). K.C. and H.L. were supported by the Dell Foundation. M.L.L. is a CPRIT scholar and is supported by ALA. This work was also supported by an NCI center grant (CA016672). A.U. is a Rosalie B. Hite Fellow.

Author information

Affiliations

  1. The University of Texas MD Anderson Cancer Center, Department of Genetics, Houston, Texas 77030, USA

    • Yong Wang
    • , Jill Waters
    • , Marco L. Leung
    • , Anna Unruh
    • , Whijae Roh
    • , Xiuqing Shi
    • , Asha Multani
    •  & Nicholas E. Navin
  2. The University of Texas Graduate School of Biomedical Sciences, Houston, Texas 77030, USA

    • Marco L. Leung
    • , Paul Scheet
    • , Selina Vattathil
    •  & Nicholas E. Navin
  3. The University of Texas MD Anderson Cancer Center, Department of Bioinformatics and Computational Biology, Houston, Texas 77030, USA

    • Ken Chen
    • , Han Liang
    •  & Nicholas E. Navin
  4. The University of Texas MD Anderson Cancer Center, Department of Epidemiology, Houston, Texas 77030, USA

    • Paul Scheet
    •  & Selina Vattathil
  5. The University of Texas MD Anderson Cancer Center, Department of Pathology, Houston, Texas 77030, USA

    • Hong Zhang
  6. Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, and Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02215, USA

    • Rui Zhao
    •  & Franziska Michor
  7. The University of Texas MD Anderson Cancer Center Department of Investigational Cancer Therapeutics, Houston, Texas 77030, USA

    • Funda Meric-Bernstam

Authors

  1. Search for Yong Wang in:

  2. Search for Jill Waters in:

  3. Search for Marco L. Leung in:

  4. Search for Anna Unruh in:

  5. Search for Whijae Roh in:

  6. Search for Xiuqing Shi in:

  7. Search for Ken Chen in:

  8. Search for Paul Scheet in:

  9. Search for Selina Vattathil in:

  10. Search for Han Liang in:

  11. Search for Asha Multani in:

  12. Search for Hong Zhang in:

  13. Search for Rui Zhao in:

  14. Search for Franziska Michor in:

  15. Search for Funda Meric-Bernstam in:

  16. Search for Nicholas E. Navin in:

Contributions

Y.W. performed experiments and data analysis. M.L.L., J.W., A.M. and X.S. performed experiments. A.U., W.R., K.C., H.L., P.S. and S.V. performed data and statistical analyses. H.Z. and F.M.-B. obtained clinical samples. R.Z. and F.M. performed modelling. N.E.N. performed experiments, analysed data and wrote the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Nicholas E. Navin.

The data from this study has been deposited into the Sequence Read Archive (SRA053195).

Extended data

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    This file contains Supplementary Tables 1-8.

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nature13600

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.