Deconvolving tumor purity and ploidy by integrating copy number alterations and loss of heterozygosity.
BACKGROUND
Next-generation sequencing (NGS) has revolutionized the study of cancer genomes. However, the reads obtained from NGS of tumor samples often consist of a mixture of normal and tumor cells, which themselves can be of multiple clonal types. A prominent problem in the analysis of cancer genome sequencing data is deconvolving the mixture to identify the reads associated with tumor cells or a particular subclone of tumor cells. Solving the problem is, however, challenging because of the so-called 'identifiability problem', where different combinations of tumor purity and ploidy often explain the sequencing data equally well.


RESULTS
We propose a new model to resolve the identifiability problem by integrating two types of sequencing information-somatic copy number alterations and loss of heterozygosity-within a unified probabilistic framework. We derive algorithms to solve our model, and implement them in a software package called PyLOH. We benchmark the performance of PyLOH using both simulated data and 12 breast cancer sequencing datasets and show that PyLOH outperforms existing methods in disambiguating the identifiability problem and estimating tumor purity.


METHODS
The PyLOH package is written in Python and is publicly available at https://github.com/uci-cbcl/PyLOH.


BACKGROUND
xhx@ics.uci.edu


BACKGROUND
Supplementary data are available at Bioinformatics online.
Department of Computer Science, Institute for Genomics and Bioinformatics and Center for Machine Learning and Intelligent Systems, University of California, Irvine, CA 92697, USA.
Yi
Y
Li
Department of Computer Science, Institute for Genomics and Bioinformatics and Center for Machine Learning and Intelligent Systems, University of California, Irvine, CA 92697, USADepartment of Computer Science, Institute for Genomics and Bioinformatics and Center for Machine Learning and Intelligent Systems, University of California, Irvine, CA 92697, USADepartment of Computer Science, Institute for Genomics and Bioinformatics and Center for Machine Learning and Intelligent Systems, University of California, Irvine, CA 92697, USA.
Xiaohui
X
Xie
D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics / Q000473:pathology; D002874:Chromosome Mapping; D019295:Computational Biology / Q000379:methods*; D056915:DNA Copy Number Variations; D015894:Genome, Human / Q000235:genetics; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D019656:Loss of Heterozygosity; D015233:Models, Statistical; D009369:Neoplasms / Q000235:genetics* / Q000473:pathology*; D011003:Ploidies; D012984:Software