TreeClone: Reconstruction of Tumor Subclone Phylogeny Based on Mutation Pairs using Next Generation Sequencing Data

TL;DR

TreeClone is a Bayesian method that reconstructs tumor subclone phylogenies using mutation pairs from next-generation sequencing data, improving inference by incorporating phylogenetic tree priors.

Contribution

It introduces a novel approach utilizing mutation pairs and a phylogenetic tree prior within a Bayesian framework for tumor subclone reconstruction.

Findings

Accurately infers subclone genotypes and proportions.

Effectively reconstructs phylogenetic trees from simulated and real data.

Outperforms existing methods in validation tests.

Abstract

We present TreeClone, a latent feature allocation model to reconstruct tumor subclones subject to phylogenetic evolution that mimics tumor evolution. Similar to most current methods, we consider data from next-generation sequencing of tumor DNA. Unlike most methods that use information in short reads mapped to single nucleotide variants (SNVs), we consider subclone phylogeny reconstruction using pairs of two proximal SNVs that can be mapped by the same short reads. As part of the Bayesian inference model, we construct a phylogenetic tree prior. The use of the tree structure in the prior greatly strengthens inference. Only subclones that can be explained by a phylogenetic tree are assigned non-negligible probabilities. The proposed Bayesian framework implies posterior distributions on the number of subclones, their genotypes, cellular proportions, and the phylogenetic tree spanned by the inferred subclones. The proposed method is validated against different sets of simulated and real-world data using single and multiple tumor samples. An open source software package is available at http://www.compgenome.org/treeclone.