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Bioinformatics. 2018 Dec 1;34(23):4017-4026. doi: 10.1093/bioinformatics/bty469.

Predicting clone genotypes from tumor bulk sequencing of multiple samples.

Miura S1,2, Gomez K1,2,3, Murillo O1,4, Huuki LA1,2, Vu T1,2, Buturla T1,2, Kumar S1,2,5.

Author information

1
Institute for Genomics and Evolutionary Medicine.
2
Department of Biology, Temple University, Philadelphia, PA, USA.
3
College of Physicians and Surgeons, Columbia University, New York, NY, USA.
4
Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
5
Center for Excellence in Genome Medicine and Research, King Abdulaziz University, Jeddah, Saudi Arabia.

Abstract

Motivation:

Analyses of data generated from bulk sequencing of tumors have revealed extensive genomic heterogeneity within patients. Many computational methods have been developed to enable the inference of genotypes of tumor cell populations (clones) from bulk sequencing data. However, the relative and absolute accuracy of available computational methods in estimating clone counts and clone genotypes is not yet known.

Results:

We have assessed the performance of nine methods, including eight previously-published and one new method (CloneFinder), by analyzing computer simulated datasets. CloneFinder, LICHeE, CITUP and cloneHD inferred clone genotypes with low error (<5% per clone) for a majority of datasets in which the tumor samples contained evolutionarily-related clones. Computational methods did not perform well for datasets in which tumor samples contained mixtures of clones from different clonal lineages. Generally, the number of clones was underestimated by cloneHD and overestimated by PhyloWGS, and BayClone2, Canopy and Clomial required prior information regarding the number of clones. AncesTree and Canopy did not produce results for a large number of datasets. Overall, the deconvolution of clone genotypes from single nucleotide variant (SNV) frequency differences among tumor samples remains challenging, so there is a need to develop more accurate computational methods and robust software for clone genotype inference.

Availability and implementation:

CloneFinder is implemented in Python and is available from https://github.com/gstecher/CloneFinderAPI.

Supplementary information:

Supplementary data are available at Bioinformatics online.

PMID:
29931046
PMCID:
PMC6247940
[Available on 2019-12-01]
DOI:
10.1093/bioinformatics/bty469

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