PhyloWGS: Reconstructing subclonal composition and evolution from whole-genome sequencing of tumors

Abstract

Tumors often contain multiple subpopulations of cancerous cells defined by distinct somatic mutations. We describe a new method, PhyloWGS, which can be applied to whole-genome sequencing data from one or more tumor samples to reconstruct complete genotypes of these subpopulations based on variant allele frequencies (VAFs) of point mutations and population frequencies of structural variations. We introduce a principled phylogenic correction for VAFs in loci affected by copy number alterations and we show that this correction greatly improves subclonal reconstruction compared to existing methods. PhyloWGS is free, open-source software, available at https://github.com/morrislab/phylowgs.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0602-8) contains supplementary material, which is available to authorized users.

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

“…To deal with copy number changes, El-Kebir et al (27) proposed instead an infinite-alleles assumption, or the multistate perfect phylogeny, where a mutation may change state more than once on the tree due to gain or loss of copy number, but changes to the same state at most once. Furthermore, Deshwar et al (20) introduces the "weak parsimony" assumption, which posits that mutations with similar CCFs across all samples lie on the same branch segment in the phylogeny. Canopy relies on both the infinite-sites assumption and the weak parsimony assumption, but takes a different approach from El-Kebir et al (27) in modeling CNAs: Canopy extends the infinite-sites assumption to CNAs by assuming that copy number events with the same breakpoints and the same copy number across all samples must be the result of a single CNA event that occurs exactly once in the tumor's evolution.…”

“…That is, each mutation appears only once and once it appears, it does not revert back to its original state. This no-homoplasy assumption, also referred to as the infinite-sites assumption (20,35), is adopted by most methods to allow model identifiability. For example, it is possible to assert that under the infinite-sites assumption, mutations with lower CCFs cannot be ancestral to mutations with higher CCFs.…”

“…To more accurately quantify the relationship between VAF and MCF, which accounts for the possible phases and temporal orders of overlapping CNAs and SNAs, we consider separately each of the three possible underlying scenarios, which were first delineated by CHAT (19) and PhyloWGS (20): (i) the CNA is ancestral to the SNA (Fig. 2A); (ii) the CNA and SNA occur in separate branches of the tree and thus affect separate clones (Fig.…”

“…Here, we propose a method, Canopy, to assess intratumor heterogeneity and infer clonal evolutionary history. The distinguishing features of Canopy compared with existing methods are as follows: (i) SNAs and CNAs are jointly modeled and overlapping events are phased and temporally ordered; (ii) the SNA input can be taken directly from the UnifiedGenotyper in GATK (30) or MuTect (31) and the CNA input are continuous-valued allele-specific copy number ratios, which can be directly obtained from allele-specific copy number estimation methods (28); (iii) a preclustering initialization step for SNAs improves robustness to noise and significantly reduces computation time; (iv) CNA events are allowed to be subclonal (19,20); (v) overlapping and nested CNA events with different breakpoints affecting the same region are treated as separate evolutionary events, as illustrated by our analysis of MDA-MB-231; (vi) the Bayesian framework reconstructs the phylogeny together with posterior confidence assessment, which is useful when the data support multiple configurations.…”

“…One practical drawback of Clomial (25) is that it takes only SNA input and assumes that all mutational loci are heterozygous from copy numberneutral regions. SCHISM (24), BitPhylogeny (26), PhyloWGS (20), and SPRUCE (27) adjust for CNAs in their model in different ways, but these methods still require limiting assumptions and do not make full use of the data, as we discuss in detail a bit later.…”