THetA: inferring intra-tumor heterogeneity from high-throughput DNA sequencing data

Layla Oesper,Benjamin J Raphael,Ahmad Mahmoody

doi:10.1186/gb-2013-14-7-r80

Layla Oesper, Benjamin J Raphael + Show 1 more

Open Access

https://doi.org/10.1186/gb-2013-14-7-r80

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Journal: Genome Biology	Publication Date: Jan 1, 2013
Citations: 239	License type: cc-by

Affiliation: Brown University, Providence College

Abstract

Tumor samples are typically heterogeneous, containing admixture by normal, non-cancerous cells and one or more subpopulations of cancerous cells. Whole-genome sequencing of a tumor sample yields reads from this mixture, but does not directly reveal the cell of origin for each read. We introduce THetA (Tumor Heterogeneity Analysis), an algorithm that infers the most likely collection of genomes and their proportions in a sample, for the case where copy number aberrations distinguish subpopulations. THetA successfully estimates normal admixture and recovers clonal and subclonal copy number aberrations in real and simulated sequencing data. THetA is available at http://compbio.cs.brown.edu/software/.

Highlights

Cancer is a disease driven in part by somatic mutations, which accumulate during the lifetime of an individual
We focus on copy number aberrations in order to estimate tumor purity and subpopulations
These intervals are identified by examining the density, or depth, of reads aligning to each location in the reference [32,33,34], and/or by clustering discordant paired reads that identify the breakpoints of copy number aberrations or other rearrangements [35,36,37,38,39,40]

Summary

Introduction

Cancer is a disease driven in part by somatic mutations, which accumulate during the lifetime of an individual. A tumor is a heterogeneous population of cells, each cell potentially containing a different complement of somatic mutations These include both clonal mutations from the founder cell or early rounds of clonal expansion and subclonal mutations that occurred after the most recent clonal expansion. The tumor purity of a Traditionally, tumor purity was assessed by visual analysis of tumor cells, either manually by a pathologist or via image analysis [11] Methods such as ASCAT [12] and ABSOLUTE [13] were introduced to estimate tumor purity directly from SNP array data. Both of these methods utilize the presence of copy number aberrations in cancer genomes to estimate both tumor purity and tumor ploidy, which is the number of copies of segments of chromosomes or entire chromosomes. These estimates of tumor purity and average ploidy are used in a second step to derive copy number aberrations

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