Integrative Inference of Subclonal Tumour Evolution from Single-Cell and Bulk Sequencing Data

Peer reviewed: 
Yes, item is peer reviewed.
Scholarly level: 
Graduate student (Masters)
Final version published as: 

Malikic, S., Jahn, K., Kuipers, J., Sahinalp, S. C., & Beerenwinkel, N. (2019, June 21). Integrative inference of subclonal tumour evolution from single-cell and bulk sequencing data. Retrieved from DOI: 10.1038/s41467-019-10737-5

Date created: 
Cancer genetics
Computational biology and bioinformatics
Tumour heterogeneity

Understanding the clonal architecture and evolutionary history of a tumour poses one of the key challenges to overcome treatment failure due to resistant cell populations. Previously, studies on subclonal tumour evolution have been primarily based on bulk sequencing and in some recent cases on single-cell sequencing data. Either data type alone has shortcomings with regard to this task, but methods integrating both data types have been lacking. Here, we present B-SCITE, the first computational approach that infers tumour phylogenies from combined single-cell and bulk sequencing data. Using a comprehensive set of simulated data, we show that B-SCITE systematically outperforms existing methods with respect to tree reconstruction accuracy and subclone identification. B-SCITE provides high-fidelity reconstructions even with a modest number of single cells and in cases where bulk allele frequencies are affected by copy number changes. On real tumour data, B-SCITE generated mutation histories show high concordance with expert generated trees.

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