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Tumours develop in an evolutionary process, in which the accumulation ofmutations produces subpopulations of cells with distinct mutational profiles,called clones. This process leads to the genetic heterogeneity widely observedin tumour sequencing data, but identifying the genotypes and frequencies of thedifferent clones is still a major challenge. Here, we present Cloe, aphylogenetic latent feature model to deconvolute tumour sequencing data into aset of related genotypes. Our approach extends latent feature models by placingthe features as nodes in a latent tree. The resulting model can capture boththe acquisition and the loss of mutations, as well as episodes of convergentevolution. We establish the validity of Cloe on synthetic data and assess itsperformance on controlled biological data, comparing our reconstructions tothose of several published state-of-the-art methods. We show that our methodprovides highly accurate reconstructions and identifies the number of clones,their genotypes and frequencies even at a modest sequencing depth. As a proofof concept we apply our model to clinical data from three cases with chroniclymphocytic leukaemia, and one case with acute myeloid leukaemia.

A phylogenetic latent feature model for clonal deconvolution