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Bioinformatics. 2014 Dec 15;30(24):3532-40. doi: 10.1093/bioinformatics/btu651. Epub 2014 Oct 8.

Quantifying tumor heterogeneity in whole-genome and whole-exome sequencing data.

Author information

1
Department of Computer Science and Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA.
2
Department of Computer Science and Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA Department of Computer Science and Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA.

Abstract

MOTIVATION:

Most tumor samples are a heterogeneous mixture of cells, including admixture by normal (non-cancerous) cells and subpopulations of cancerous cells with different complements of somatic aberrations. This intra-tumor heterogeneity complicates the analysis of somatic aberrations in DNA sequencing data from tumor samples.

RESULTS:

We describe an algorithm called THetA2 that infers the composition of a tumor sample-including not only tumor purity but also the number and content of tumor subpopulations-directly from both whole-genome (WGS) and whole-exome (WXS) high-throughput DNA sequencing data. This algorithm builds on our earlier Tumor Heterogeneity Analysis (THetA) algorithm in several important directions. These include improved ability to analyze highly rearranged genomes using a variety of data types: both WGS sequencing (including low ∼7× coverage) and WXS sequencing. We apply our improved THetA2 algorithm to WGS (including low-pass) and WXS sequence data from 18 samples from The Cancer Genome Atlas (TCGA). We find that the improved algorithm is substantially faster and identifies numerous tumor samples containing subclonal populations in the TCGA data, including in one highly rearranged sample for which other tumor purity estimation algorithms were unable to estimate tumor purity.

PMID:
25297070
PMCID:
PMC4253833
DOI:
10.1093/bioinformatics/btu651
[Indexed for MEDLINE]
Free PMC Article

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