MixClone: a mixture model for inferring tumor subclonal populations

BMC Genomics

Proceedings

Yi Li¹  Xiaohui Xie² 

[1] Department of Computer Science, University of California, 92697, Irvine, CA, US;Department of Computer Science, University of California, 92697, Irvine, CA, US;Institute for Genomics and Bioinformatics, University of California, 92697, Irvine, CA, US;Center for Machine Learning and Intelligent Systems, University of California, 92697, Irvine, CA, US;

关键词: Cancer genomics;    Subclonal inference;    Whole genome sequencing;    Somatic copy number alteration;    Allele frequency;    Mixture model;   

DOI  :  10.1186/1471-2164-16-S2-S1

来源: Springer

【 摘 要 】

BackgroundTumor genomes are often highly heterogeneous, consisting of genomes from multiple subclonal types. Complete characterization of all subclonal types is a fundamental need in tumor genome analysis. With the advancement of next-generation sequencing, computational methods have recently been developed to infer tumor subclonal populations directly from cancer genome sequencing data. Most of these methods are based on sequence information from somatic point mutations, However, the accuracy of these algorithms depends crucially on the quality of the somatic mutations returned by variant calling algorithms, and usually requires a deep coverage to achieve a reasonable level of accuracy.ResultsWe describe a novel probabilistic mixture model, MixClone, for inferring the cellular prevalences of subclonal populations directly from whole genome sequencing of paired normal-tumor samples. MixClone integrates sequence information of somatic copy number alterations and allele frequencies within a unified probabilistic framework. We demonstrate the utility of the method using both simulated and real cancer sequencing datasets, and show that it significantly outperforms existing methods for inferring tumor subclonal populations. The MixClone package is written in Python and is publicly available at https://github.com/uci-cbcl/MixClone.ConclusionsThe probabilistic mixture model proposed here provides a new framework for subclonal analysis based on cancer genome sequencing data. By applying the method to both simulated and real cancer sequencing data, we show that integrating sequence information from both somatic copy number alterations and allele frequencies can significantly improve the accuracy of inferring tumor subclonal populations.

【 授权许可】

CC BY   
© Li and Xie; licensee BioMed Central Ltd. 2015