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PhyC: Clustering cancer evolutionary trees.


ABSTRACT: Multi-regional sequencing provides new opportunities to investigate genetic heterogeneity within or between common tumors from an evolutionary perspective. Several state-of-the-art methods have been proposed for reconstructing cancer evolutionary trees based on multi-regional sequencing data to develop models of cancer evolution. However, there have been few studies on comparisons of a set of cancer evolutionary trees. We propose a clustering method (phyC) for cancer evolutionary trees, in which sub-groups of the trees are identified based on topology and edge length attributes. For interpretation, we also propose a method for evaluating the sub-clonal diversity of trees in the clusters, which provides insight into the acceleration of sub-clonal expansion. Simulation showed that the proposed method can detect true clusters with sufficient accuracy. Application of the method to actual multi-regional sequencing data of clear cell renal carcinoma and non-small cell lung cancer allowed for the detection of clusters related to cancer type or phenotype. phyC is implemented with R(?3.2.2) and is available from https://github.com/ymatts/phyC.

SUBMITTER: Matsui Y 

PROVIDER: S-EPMC5432190 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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phyC: Clustering cancer evolutionary trees.

Matsui Yusuke Y   Niida Atsushi A   Uchi Ryutaro R   Mimori Koshi K   Miyano Satoru S   Shimamura Teppei T  

PLoS computational biology 20170501 5


Multi-regional sequencing provides new opportunities to investigate genetic heterogeneity within or between common tumors from an evolutionary perspective. Several state-of-the-art methods have been proposed for reconstructing cancer evolutionary trees based on multi-regional sequencing data to develop models of cancer evolution. However, there have been few studies on comparisons of a set of cancer evolutionary trees. We propose a clustering method (phyC) for cancer evolutionary trees, in which  ...[more]

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