Abstract
Somatic variants can be used as lineage markers for the phylogenetic reconstruction of cancer evolution. Since somatic phylogenetics is complicated by sample heterogeneity, novel specialized tree-building methods are required for cancer phylogeny reconstruction. We present LICHeE (Lineage Inference for Cancer Heterogeneity and Evolution), a novel method that automates the phylogenetic inference of cancer progression from multiple somatic samples. LICHeE uses variant allele frequencies of somatic single nucleotide variants obtained by deep sequencing to reconstruct multi-sample cell lineage trees and infer the subclonal composition of the samples. LICHeE is open source and available at http://viq854.github.io/lichee.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0647-8) contains supplementary material, which is available to authorized users.
Highlights
Cancer is driven by the accumulation of somatic mutations that confer fitness advantages to the tumor cells
We demonstrate that LICHeE is highly effective in reconstructing the lineage trees and sample heterogeneity by evaluating it on simulated trees of heterogeneous cancer cell lineage evolution, as well as on three recently published ultra-deep-sequencing multi-sample datasets of clear cell renal cell carcinoma by Gerlinger et al [21], high-grade serous ovarian cancer (HGSC) by Bashashati et al [27], and breast cancer xenoengraftment in immunodeficient mice by Eirew et al [32], for which single-cell validation results are available
On the HGSC datasets we show that the trees generated by LICHeE are better supported by the data and demonstrate why applying neighbor joining with Pearson correlation distance metric, used by the study, might not be suitable for cancer datasets
Summary
Cancer is driven by the accumulation of somatic mutations that confer fitness advantages to the tumor cells. With the advent of next-generation sequencing technologies, many large-scale efforts are underway to catalog the somatic mutational events driving the progression of cancer [3,4] and infer the phylogenetic relationships of tumor subclones. Characterizing the heterogeneity and inferring tumor phylogenies are key steps for developing targeted cancer therapies [5] and understanding the biology and progression of cancer. Studies have utilized variant allele frequency (VAF) data of somatic single nucleotide variants (SSNVs) obtained by whole-genome [6,7], exome [8], and targeted deep sequencing [6,9].
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