Brief Communication | Published:

PyClone: statistical inference of clonal population structure in cancer

Nature Methods volume 11, pages 396398 (2014) | Download Citation

Abstract

We introduce PyClone, a statistical model for inference of clonal population structures in cancers. PyClone is a Bayesian clustering method for grouping sets of deeply sequenced somatic mutations into putative clonal clusters while estimating their cellular prevalences and accounting for allelic imbalances introduced by segmental copy-number changes and normal-cell contamination. Single-cell sequencing validation demonstrates PyClone's accuracy.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    Science 194, 23–28 (1976).

  2. 2.

    & N. Engl. J. Med. 368, 842–851 (2013).

  3. 3.

    & Nature 481, 306–313 (2012).

  4. 4.

    et al. Nature 486, 395–399 (2012).

  5. 5.

    et al. Nature 481, 506–510 (2012).

  6. 6.

    et al. Cell 149, 994–1007 (2012).

  7. 7.

    et al. Nat. Biotechnol. 30, 413–421 (2012).

  8. 8.

    et al. Cell 150, 1121–1134 (2012).

  9. 9.

    et al. Nature 461, 809–813 (2009).

  10. 10.

    et al. N. Engl. J. Med. 366, 883–892 (2012).

  11. 11.

    The 1000 Genomes Project Consortium. Nature 467, 1061–1073 (2010).

  12. 12.

    et al. Genome Biol. 12, R124 (2011).

  13. 13.

    & in Proc. 2007 Joint Conf. Empir. Methods Natural Lang. Process. Comput. Natural Lang. Learn. (EMNLP-CoNLL) Vol. 410, 420 (2007).

  14. 14.

    et al. J. Pathol. 231, 21–34 (2013).

  15. 15.

    et al. Sci. Transl. Med. 4, 136ra68 (2012).

  16. 16.

    et al. N. Engl. J. Med. 368, 1199–1209 (2013).

  17. 17.

    et al. Proc. Natl. Acad. Sci. USA 110, 4009–4014 (2013).

  18. 18.

    & Bayesian Anal. 4, 367–392 (2009).

  19. 19.

    et al. Nature 461, 272–276 (2009).

  20. 20.

    et al. Proc. Natl. Acad. Sci. USA 107, 16910–16915 (2010).

  21. 21.

    et al. Biostatistics 11, 164–175 (2010).

  22. 22.

    et al. Genome Biol. 11, R92 (2010).

  23. 23.

    et al. Nucleic Acids Res. 40, e115 (2012).

  24. 24.

    & Bioinformatics 26, 589–595 (2010).

Download references

Acknowledgements

This work is funded by Canadian Institutes for Health Research (CIHR), Genome Canada, Genome British Columbia, Canadian Cancer Society Research Institute and Canadian Breast Cancer Foundation grants to S.P.S. and S.A. S.P.S. is supported by the Michael Smith Foundation for Health Research and is the Canada Research Chair (CRC) for Computational Cancer Genomics. S.A. is the CRC for Molecular Oncology. A.R. is supported by a CIHR Banting scholarship.

Author information

Affiliations

  1. Bioinformatics Graduate Program, University of British Columbia, Vancouver, British Columbia, Canada.

    • Andrew Roth
    •  & Gavin Ha
  2. Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada.

    • Andrew Roth
    • , Jaswinder Khattra
    • , Damian Yap
    • , Adrian Wan
    • , Emma Laks
    • , Justina Biele
    • , Gavin Ha
    • , Samuel Aparicio
    •  & Sohrab P Shah
  3. Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada.

    • Samuel Aparicio
    •  & Sohrab P Shah
  4. Department of Statistics, University of British Columbia, Vancouver, British Columbia, Canada.

    • Alexandre Bouchard-Côté

Authors

  1. Search for Andrew Roth in:

  2. Search for Jaswinder Khattra in:

  3. Search for Damian Yap in:

  4. Search for Adrian Wan in:

  5. Search for Emma Laks in:

  6. Search for Justina Biele in:

  7. Search for Gavin Ha in:

  8. Search for Samuel Aparicio in:

  9. Search for Alexandre Bouchard-Côté in:

  10. Search for Sohrab P Shah in:

Contributions

Project conception and oversight: S.P.S., S.A., A.R.; method development: A.R., A.B.-C., S.P.S.; implementation and benchmarking: A.R.; manuscript writing and editing, study design and execution: A.R., A.B.C., S.P.S., S.A.; single-cell sequencing: J.K., D.Y., A.W., E.L., J.B.; data analysis and interpretation: G.H.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Sohrab P Shah.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–14, Supplementary Results, Supplementary Discussion and Supplementary Note

Excel files

  1. 1.

    Supplementary Table 1

    Allelic counts, IBBMM and PyClone PCN cellular prevalence estimates for mutations in high grade serous ovarian cancer case 2. Copy number predictions where inferred using PICNIC as described in the Online Methods. Cellular prevalences where computed by taking the mean of the post burnin trace for the cellular prevalences for the respective methods. The standard deviation of the cellular prevalence parameter estimated from the post burnin trace is also included. Cluster ids (last two columns) were predicted from the post burnin trace using the MPEAR clustering criteria as described in the Online Methods and Supplementary Note. Mutation ids list gene name, chromosome and chromosome coordinate. All coordinates are in the hg19 coordinate system.

  2. 2.

    Supplementary Table 2

    Allelic counts, IBBMM and PyClone PCN cellular prevalence estimates for mutations in high grade serous ovarian cancer case 1. Copy number predictions where inferred using PICNIC as described in the Online Methods. Cellular prevalences where computed by taking the mean of the post burnin trace for the cellular prevalences for the respective methods. The standard deviation of the cellular prevalence parameter estimated from the post burnin trace is also included. Cluster ids (last two columns) were predicted from the post burnin trace using the MPEAR clustering criteria as described in the Online Methods and Supplementary Note. Mutation ids list gene name, chromosome and chromosome coordinate. All coordinates are in the hg19 coordinate system.

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nmeth.2883

Further reading