Open Access
June 2017 Quantification of multiple tumor clones using gene array and sequencing data
Yichen Cheng, James Y. Dai, Thomas G. Paulson, Xiaoyu Wang, Xiaohong Li, Brian J. Reid, Charles Kooperberg
Ann. Appl. Stat. 11(2): 967-991 (June 2017). DOI: 10.1214/17-AOAS1026


Cancer development is driven by genomic alterations, including copy number aberrations. The detection of copy number aberrations in tumor cells is often complicated by possible contamination of normal stromal cells in tumor samples and intratumor heterogeneity, namely the presence of multiple clones of tumor cells. In order to correctly quantify copy number aberrations, it is critical to successfully de-convolute the complex structure of the genetic information from tumor samples. In this article, we propose a general Bayesian method for estimating copy number aberrations when there are normal cells and potentially more than one tumor clones. Our method provides posterior probabilities for the proportions of tumor clones and normal cells. We incorporate prior information on the distribution of the copy numbers to prioritize biologically more plausible solutions and alleviate possible identifiability issues that have been observed by many researchers. Our model is flexible and can work for both SNP array and next-generation sequencing data. We compare our method to existing ones and illustrate the advantage of our approach in multiple datasets.


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Yichen Cheng. James Y. Dai. Thomas G. Paulson. Xiaoyu Wang. Xiaohong Li. Brian J. Reid. Charles Kooperberg. "Quantification of multiple tumor clones using gene array and sequencing data." Ann. Appl. Stat. 11 (2) 967 - 991, June 2017.


Received: 1 May 2016; Revised: 1 October 2016; Published: June 2017
First available in Project Euclid: 20 July 2017

zbMATH: 06775900
MathSciNet: MR3693554
Digital Object Identifier: 10.1214/17-AOAS1026

Keywords: BIC , Copy number aberration , Identifiability , intratumor heterogeneity

Rights: Copyright © 2017 Institute of Mathematical Statistics

Vol.11 • No. 2 • June 2017
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