Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Highly Accessed Methodology article

Inferring clonal evolution of tumors from single nucleotide somatic mutations

Wei Jiao12, Shankar Vembu3, Amit G Deshwar4, Lincoln Stein12 and Quaid Morris13456*

Author Affiliations

1 Department of Molecular Genetics, University of Toronto, Toronto, Canada

2 Ontario Institute for Cancer Research, Toronto, Canada

3 Donnelly Center for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada

4 Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada

5 Banting and Best Department of Medical Research, University of Toronto, Toronto, Canada

6 Department of Computer Science, University of Toronto, Toronto, Canada

For all author emails, please log on.

BMC Bioinformatics 2014, 15:35  doi:10.1186/1471-2105-15-35

Published: 1 February 2014

Abstract

Background

High-throughput sequencing allows the detection and quantification of frequencies of somatic single nucleotide variants (SNV) in heterogeneous tumor cell populations. In some cases, the evolutionary history and population frequency of the subclonal lineages of tumor cells present in the sample can be reconstructed from these SNV frequency measurements. But automated methods to do this reconstruction are not available and the conditions under which reconstruction is possible have not been described.

Results

We describe the conditions under which the evolutionary history can be uniquely reconstructed from SNV frequencies from single or multiple samples from the tumor population and we introduce a new statistical model, PhyloSub, that infers the phylogeny and genotype of the major subclonal lineages represented in the population of cancer cells. It uses a Bayesian nonparametric prior over trees that groups SNVs into major subclonal lineages and automatically estimates the number of lineages and their ancestry. We sample from the joint posterior distribution over trees to identify evolutionary histories and cell population frequencies that have the highest probability of generating the observed SNV frequency data. When multiple phylogenies are consistent with a given set of SNV frequencies, PhyloSub represents the uncertainty in the tumor phylogeny using a “partial order plot”. Experiments on a simulated dataset and two real datasets comprising tumor samples from acute myeloid leukemia and chronic lymphocytic leukemia patients demonstrate that PhyloSub can infer both linear (or chain) and branching lineages and its inferences are in good agreement with ground truth, where it is available.

Conclusions

PhyloSub can be applied to frequencies of any “binary” somatic mutation, including SNVs as well as small insertions and deletions. The PhyloSub and partial order plot software is available from https://github.com/morrislab/phylosub/ webcite.