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June 2019 TreeClone: Reconstruction of tumor subclone phylogeny based on mutation pairs using next generation sequencing data
Tianjian Zhou, Subhajit Sengupta, Peter Müller, Yuan Ji
Ann. Appl. Stat. 13(2): 874-899 (June 2019). DOI: 10.1214/18-AOAS1224

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.

Citation

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Tianjian Zhou. Subhajit Sengupta. Peter Müller. Yuan Ji. "TreeClone: Reconstruction of tumor subclone phylogeny based on mutation pairs using next generation sequencing data." Ann. Appl. Stat. 13 (2) 874 - 899, June 2019. https://doi.org/10.1214/18-AOAS1224

Information

Received: 1 October 2017; Revised: 1 August 2018; Published: June 2019
First available in Project Euclid: 17 June 2019

zbMATH: 1423.62155
MathSciNet: MR3963556
Digital Object Identifier: 10.1214/18-AOAS1224

Keywords: latent feature model , mutation pair , NGS data , phylogenetic tree , subclone , tumor heterogeneity

Rights: Copyright © 2019 Institute of Mathematical Statistics

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Vol.13 • No. 2 • June 2019
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