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

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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Figure 1: Validation of the copy-number analysis of Sclust against Absolute and a mixing series of the small-cell lung-cancer cell line H2171 with its matched normal cell line.
Figure 2: Reconstruction of the subclonal structure of the breast cancer case PD4120a.
Figure 3: Simulations of a clonal and subclonal population with different proportions of clonal mutations.
Figure 4: Overview of the Sclust workflow.
Figure 5: Example of the first two pages of the <sample>_cn_profile.pdf file.

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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).

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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.

Corresponding author

Correspondence to Martin Peifer.

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The authors declare no competing financial interests.

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Cun, Y., Yang, T., Achter, V. et al. Copy-number analysis and inference of subclonal populations in cancer genomes using Sclust. Nat Protoc 13, 1488–1501 (2018). https://doi.org/10.1038/nprot.2018.033

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