PT  - JOURNAL ARTICLE
AU  - Salem Malikic
AU  - Katharina Jahn
AU  - Jack Kuipers
AU  - S. Cenk Sahinalp
AU  - Niko Beerenwinkel
TI  - Integrative inference of subclonal tumour evolution from single-cell and bulk sequencing data
AID  - 10.1101/234914
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 234914
4099  - http://biorxiv.org/content/early/2017/12/15/234914.short
4100  - http://biorxiv.org/content/early/2017/12/15/234914.full
AB  - Understanding the evolutionary history and subclonal composition of a tumour represents one of the key challenges in overcoming treatment failure due to resistant cell populations. Most of the current data on tumour genetics stems from short read bulk sequencing data. While this type of data is characterised by low sequencing noise and cost, it consists of aggregate measurements across a large number of cells. It is therefore of limited use for the accurate detection of the distinct cellular populations present in a tumour and the unambiguous inference of their evolutionary relationships. Single-cell DNA sequencing instead provides data of the highest resolution for studying intra-tumour heterogeneity and evolution, but is characterised by higher sequencing costs and elevated noise rates. In this work, we develop the first computational approach that infers trees of tumour evolution from combined single-cell and bulk sequencing data. Using a comprehensive set of simulated data, we show that our approach systematically outperforms existing methods with respect to tree reconstruction accuracy and subclone identification. High fidelity reconstructions are obtained even with a modest number of single cells. We also show that combining single-cell and bulk sequencing data provides more realistic mutation histories for real tumours.