  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.
