基于系统发育推断多个肿瘤样本中的克隆性。

Clonality inference in multiple tumor samples using phylogeny.

机构信息

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.

出版信息

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

DOI:10.1093/bioinformatics/btv003

PMID:25568283

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.

摘要

动机

肿瘤内异质性通过癌症进展过程中亚克隆的进化表现出来。尽管最近的研究表明这种异质性具有临床意义，但在计算机上确定克隆亚群仍然是一个挑战。

结果

我们通过一种新的组合方法来解决这个问题，该方法名为使用系统发生学推断肿瘤中的克隆种群（clonality inference in tumors using phylogeny，CITUP），该方法在满足系统发生学约束的同时，推断克隆种群及其频率，并能够利用来自多个样本的数据。使用模拟数据集和来自两项癌症研究的深度测序数据，我们表明 CITUP 可以非常准确地预测克隆频率和潜在的系统发生关系。