PT - JOURNAL ARTICLE AU - Yusuke Matsui AU - Atsushi Niida AU - Ryutaro Uchi AU - Koshi Mimori AU - Satoru Miyano AU - Teppei Shimamura TI - phyC: Clustering cancer evolutionary trees AID - 10.1101/069302 DP - 2016 Jan 01 TA - bioRxiv PG - 069302 4099 - http://biorxiv.org/content/early/2016/08/12/069302.short 4100 - http://biorxiv.org/content/early/2016/08/12/069302.full AB - Motivation 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 sub-clonal evolutionary trees based on multi-regional sequencing data to develop models of cancer evolution. However, the methods developed thus far are not sufficient to characterize and interpret the diversity of cancer sub-clonal evolutionary trees.Results We propose a clustering method (phyC) for cancer sub-clonal 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 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.Availability phyC is implemented with R(>=3.2.2) and is available from https://github.com/ymatts/phyC.Contact ymatsui{at}med.nagoya-u.ac.jp