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BMC Bioinformatics. 2014 Jan 24;15:27. doi: 10.1186/1471-2105-15-27.

Using single cell sequencing data to model the evolutionary history of a tumor.

Author information

1
Biometric Research Branch, National Cancer Institute, 9609 Medical Center Dr,, MSC 9735 Bethesda, MD 20892, USA. rsimon@mail.nih.gov.

Abstract

BACKGROUND:

The introduction of next-generation sequencing (NGS) technology has made it possible to detect genomic alterations within tumor cells on a large scale. However, most applications of NGS show the genetic content of mixtures of cells. Recently developed single cell sequencing technology can identify variation within a single cell. Characterization of multiple samples from a tumor using single cell sequencing can potentially provide information on the evolutionary history of that tumor. This may facilitate understanding how key mutations accumulate and evolve in lineages to form a heterogeneous tumor.

RESULTS:

We provide a computational method to infer an evolutionary mutation tree based on single cell sequencing data. Our approach differs from traditional phylogenetic tree approaches in that our mutation tree directly describes temporal order relationships among mutation sites. Our method also accommodates sequencing errors. Furthermore, we provide a method for estimating the proportion of time from the earliest mutation event of the sample to the most recent common ancestor of the sample of cells. Finally, we discuss current limitations on modeling with single cell sequencing data and possible improvements under those limitations.

CONCLUSIONS:

Inferring the temporal ordering of mutational sites using current single cell sequencing data is a challenge. Our proposed method may help elucidate relationships among key mutations and their role in tumor progression.

PMID:
24460695
PMCID:
PMC3903814
DOI:
10.1186/1471-2105-15-27
[Indexed for MEDLINE]
Free PMC Article

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