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June 2019 Phylogeny-based tumor subclone identification using a Bayesian feature allocation model
Li Zeng, Joshua L. Warren, Hongyu Zhao
Ann. Appl. Stat. 13(2): 1212-1241 (June 2019). DOI: 10.1214/18-AOAS1223


Tumor cells acquire different genetic alterations during the course of evolution in cancer patients. As a result of competition and selection, only a few subgroups of cells with distinct genotypes survive. These subgroups of cells are often referred to as subclones. In recent years, many statistical and computational methods have been developed to identify tumor subclones, leading to biologically significant discoveries and shedding light on tumor progression, metastasis, drug resistance and other processes. However, most existing methods are either not able to infer the phylogenetic structure among subclones, or not able to incorporate copy number variations (CNV). In this article, we propose SIFA (tumor Subclone Identification by Feature Allocation), a Bayesian model which takes into account both CNV and tumor phylogeny structure to infer tumor subclones. We compare the performance of SIFA with two other commonly used methods using simulation studies with varying sequencing depth, evolutionary tree size, and tree complexity. SIFA consistently yields better results in terms of Rand Index and cellularity estimation accuracy. The usefulness of SIFA is also demonstrated through its application to whole genome sequencing (WGS) samples from four patients in a breast cancer study.


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Li Zeng. Joshua L. Warren. Hongyu Zhao. "Phylogeny-based tumor subclone identification using a Bayesian feature allocation model." Ann. Appl. Stat. 13 (2) 1212 - 1241, June 2019.


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

zbMATH: 1423.62154
MathSciNet: MR3963569
Digital Object Identifier: 10.1214/18-AOAS1223

Keywords: Intra-tumor heterogeneity , latent feature allocation , Model selection , tumor evolution

Rights: Copyright © 2019 Institute of Mathematical Statistics


Vol.13 • No. 2 • June 2019
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