Protocol | Published:

Copy-number analysis and inference of subclonal populations in cancer genomes using Sclust

Nature Protocols volume 13, pages 14881501 (2018) | Download Citation

Abstract

The genomes of cancer cells constantly change during pathogenesis. This evolutionary process can lead to the emergence of drug-resistant mutations in subclonal populations, which can hinder therapeutic intervention in patients. Data derived from massively parallel sequencing can be used to infer these subclonal populations using tumor-specific point mutations. The accurate determination of copy-number changes and tumor impurity is necessary to reliably infer subclonal populations by mutational clustering. This protocol describes how to use Sclust, a copy-number analysis method with a recently developed mutational clustering approach. In a series of simulations and comparisons with alternative methods, we have previously shown that Sclust accurately determines copy-number states and subclonal populations. Performance tests show that the method is computationally efficient, with copy-number analysis and mutational clustering taking <10 min. Sclust is designed such that even non-experts in computational biology or bioinformatics with basic knowledge of the Linux/Unix command-line syntax should be able to carry out analyses of subclonal populations.

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Acknowledgements

We thank the Evolution and Heterogeneity Working Group of the PCAWG initiative for fruitful discussions; P. van Loo, D. Wedge, and S. Dentro for making the Battenberg calls for PD4120a available; and L. Maas for proofreading. The computation was performed on the DFG-funded CHEOPS Cluster of the Regional Computing Centre of Cologne. This work was supported by German Cancer Aid (Deutsche Krebshilfe, grant ID: 109679), the German Ministry of Science and Education (BMBF) as part of the e:Med program (grant nos. 01ZX1303A and 01ZX1406), and the Deutsche Forschungsgemeinschaft (CRU-286, CP2).

Author information

Author notes

    • Yupeng Cun
    • , Tsun-Po Yang
    •  & Viktor Achter

    These authors contributed equally to this work.

Affiliations

  1. Department of Translational Genomics, Center for Integrated Oncology Cologne–Bonn, Medical Faculty, University of Cologne, Cologne, Germany.

    • Yupeng Cun
    • , Tsun-Po Yang
    •  & Martin Peifer
  2. Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany.

    • Tsun-Po Yang
    •  & Martin Peifer
  3. Computing Center, University of Cologne, Cologne, Germany.

    • Viktor Achter
    •  & Ulrich Lang
  4. Department of Informatics, University of Cologne, Cologne, Germany.

    • Ulrich Lang

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Contributions

Y.C. and M.P. conceived the project. Y.C., T.-P.Y., V.A., and M.P. wrote the manuscript. Y.C., T.-P.Y., V.A., and M.P. developed and optimized the algorithm. Y.C., T.-P.Y., V.A., and M.P. performed computational analysis. V.A. and U.L. provided and optimized computing and data infrastructure. All co-authors reviewed the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Martin Peifer.

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https://doi.org/10.1038/nprot.2018.033

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