Single-cell mutation identification via phylogenetic inference

Jochen Singer, Jack Kuipers, Katharina Jahn and Niko Beerenwinkel ()
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Jochen Singer: ETH Zurich
Jack Kuipers: ETH Zurich
Katharina Jahn: ETH Zurich
Niko Beerenwinkel: ETH Zurich

Nature Communications, 2018, vol. 9, issue 1, 1-8

Abstract: Abstract Reconstructing the evolution of tumors is a key aspect towards the identification of appropriate cancer therapies. The task is challenging because tumors evolve as heterogeneous cell populations. Single-cell sequencing holds the promise of resolving the heterogeneity of tumors; however, it has its own challenges including elevated error rates, allelic drop-out, and uneven coverage. Here, we develop a new approach to mutation detection in individual tumor cells by leveraging the evolutionary relationship among cells. Our method, called SCIΦ, jointly calls mutations in individual cells and estimates the tumor phylogeny among these cells. Employing a Markov Chain Monte Carlo scheme enables us to reliably call mutations in each single cell even in experiments with high drop-out rates and missing data. We show that SCIΦ outperforms existing methods on simulated data and applied it to different real-world datasets, namely a whole exome breast cancer as well as a panel acute lymphoblastic leukemia dataset.

Date: 2018
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DOI: 10.1038/s41467-018-07627-7

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