Reconstructing subclonal composition and evolution from whole genome sequencing of tumors
Tumors often contain multiple, genetically distinct subpopulations of cancerous cells. These so-called subclonal populations are defined by distinct somatic mutations that include point mutations such as single nucleotide variants and small indels ? collectively called simple somatic mutations (SSMs) ? as well as larger structural changes that result in copy number variations (CNVs). In some cases, the genotype and prevalence of these subpopulations can be reconstructed based on
... short-read sequencing of DNA in one or more tumor samples. To date, no automated SSM-based subclonal reconstructions have been attempted on WGS data; and CNV-based reconstructions are limited to tumors with two or fewer cancerous subclonal populations and with a small number of CNVs. We describe a new automated method, PhyloWGS, that can be applied to WGS data from one or more tumor samples to perform subclonal reconstruction based on both CNVs and SSMs. PhyloWGS successfully recovers the composition of mixtures of a highly rearranged TGCA cell line when a CNV-based method fails. On WGS data with average read depth of 40 from five time-series chronic lymphocytic leukemia samples, PhyloWGS recovers the same tumor phylogeny previously reconstructed using deep targeted resequencing. To further explore the limits of WGS-based subclonal reconstruction, we ran PhyloWGS on simulated data: PhyloWGS can reliably reconstruct as many as three cancerous subpopulations based on 30-50x coverage WGS data from a single tumor sample with 10?s to 1000?s of SSMs per subpopulation. At least five cancerous subpopulations can be reconstructed if provided with read depths of 200 or more. PhyloWGS is the first automated method that can be applied to WGS tumor data that accurately reconstructs the frequency, genotype and phylogeny of the subclonal populations based on both SSMs and CNVs. It also provides a principled, automated approach to combining overlapping SSM and CNV data. By demonstrating the utility of PhyloWGS on medium depth WGS data, including from examples with highly rearranged chromosomes, we have greatly expanded the range of tumors for which subclonal reconstruction is possible.