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

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/.

Assessing intratumor heterogeneity and tracking longitudinal and spatial clonal evolutionary history by next-generation sequencing

“…For example, ABSOLUTE (13), one of the earliest methods, classifies mutations as clonal or subclonal after adjusting for the estimated purity and ploidy of the sample. Most approaches for the detection of subclonal mutations treat point mutations and copy number aberrations separately (15)(16)(17)(18). In the case of point mutations, that is, single-nucleotide alterations (SNAs) and small insertions and deletions (indels), most methods rely on mixture models for the variant allele frequency (VAF) under the assumption that mutations carried by the same set of cells have the same VAF.…”

A general framework for analyzing tumor subclonality using SNP array and DNA sequencing data

“…While CHAT does not solve all the issues facing the cancer genome deconvolution problem, it attempts to overcome several important compromises or simplifying assumptions that underlie other methods. First, oncoSNP [51] and THetA [41] are designed to estimate sCNA clonality, but they do not address the clonality of somatic mutations. Second, Ding et al [52] used a kernel density estimation method to characterize somatic mutations, but only focused on those in the euploid regions of genome, staying clear of the complicated relationship between SNV and sCNAs.…”

A novel framework for analyzing somatic copy number aberrations and tumor subclones for paired heterogeneous tumor samples

“…At last, Bayesian information criterion (BIC) was used to guide model selection for final determination of the number of tumor subclones. We applied SAPPH to the simulated datasets and compared it to the results of THetA [11] to demonstrate its ability in estimating tumor subclone copy number aberrations.…”

“…Therefore the admixed normal DNA attenuates measured signals representing genomic aberrations in tumor DNA, which results in the decrease of both signal-to-noise ratios in sequencing data and the performance of aberration detection [10][11][12]. Thirdly, due to the fixed total number of reads in genome sequencing, large copy number aberrations in the part of the genome will cause the observed number of mapped reads in normal genome regions to deviate from the expected value [11]. Therefore, finding the sequencing data baseline for normal genomic state is critical for the estimation of copy numbers for other genome regions.…”