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

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

SUBMITTER: Oesper L 

PROVIDER: S-EPMC4253833 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature