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Bioinformatics. 2015 May 1;31(9):1349-56. doi: 10.1093/bioinformatics/btv003. Epub 2015 Jan 6.

Clonality inference in multiple tumor samples using phylogeny.

Author information

1
School of Computing Science, Simon Fraser University, Burnaby, BC, Canada, BC Cancer Agency, Vancouver, BC, Canada, Vancouver Prostate Center, Vancouver, BC, Canada and School of Informatics and Computing, Indiana University, Bloomington, IN, USA.
2
School of Computing Science, Simon Fraser University, Burnaby, BC, Canada, BC Cancer Agency, Vancouver, BC, Canada, Vancouver Prostate Center, Vancouver, BC, Canada and School of Informatics and Computing, Indiana University, Bloomington, IN, USA School of Computing Science, Simon Fraser University, Burnaby, BC, Canada, BC Cancer Agency, Vancouver, BC, Canada, Vancouver Prostate Center, Vancouver, BC, Canada and School of Informatics and Computing, Indiana University, Bloomington, IN, USA.

Abstract

MOTIVATION:

Intra-tumor heterogeneity presents itself through the evolution of subclones during cancer progression. Although recent research suggests that this heterogeneity has clinical implications, in silico determination of the clonal subpopulations remains a challenge.

RESULTS:

We address this problem through a novel combinatorial method, named clonality inference in tumors using phylogeny (CITUP), that infers clonal populations and their frequencies while satisfying phylogenetic constraints and is able to exploit data from multiple samples. Using simulated datasets and deep sequencing data from two cancer studies, we show that CITUP predicts clonal frequencies and the underlying phylogeny with high accuracy.

AVAILABILITY AND IMPLEMENTATION:

CITUP is freely available at: http://sourceforge.net/projects/citup/.

CONTACT:

cenk@sfu.ca

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
25568283
DOI:
10.1093/bioinformatics/btv003
[Indexed for MEDLINE]

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