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December 2016 A phylogenetic latent feature model for clonal deconvolution
Francesco Marass, Florent Mouliere, Ke Yuan, Nitzan Rosenfeld, Florian Markowetz
Ann. Appl. Stat. 10(4): 2377-2404 (December 2016). DOI: 10.1214/16-AOAS986

Abstract

Tumours develop in an evolutionary process, in which the accumulation of mutations produces subpopulations of cells with distinct mutational profiles, called clones. This process leads to the genetic heterogeneity widely observed in tumour sequencing data, but identifying the genotypes and frequencies of the different clones is still a major challenge. Here, we present Cloe, a phylogenetic latent feature model to deconvolute tumour sequencing data into a set of related genotypes. Our approach extends latent feature models by placing the features as nodes in a latent tree. The resulting model can capture both the acquisition and the loss of mutations, as well as episodes of convergent evolution. We establish the validity of Cloe on synthetic data and assess its performance on controlled biological data, comparing our reconstructions to those of several published state-of-the-art methods. We show that our method provides highly accurate reconstructions and identifies the number of clones, their genotypes and frequencies even at a modest sequencing depth. As a proof of concept, we apply our model to clinical data from three cases with chronic lymphocytic leukaemia and one case with acute myeloid leukaemia.

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Francesco Marass. Florent Mouliere. Ke Yuan. Nitzan Rosenfeld. Florian Markowetz. "A phylogenetic latent feature model for clonal deconvolution." Ann. Appl. Stat. 10 (4) 2377 - 2404, December 2016. https://doi.org/10.1214/16-AOAS986

Information

Received: 1 April 2016; Revised: 1 August 2016; Published: December 2016
First available in Project Euclid: 5 January 2017

zbMATH: 06688781
MathSciNet: MR3592061
Digital Object Identifier: 10.1214/16-AOAS986

Keywords: admixture , Clonal deconvolution , latent feature model , phylogeny , tumour heterogeneity

Rights: Copyright © 2016 Institute of Mathematical Statistics

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Vol.10 • No. 4 • December 2016
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