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bioRxiv · 10.1101/069302

phyC: Clustering cancer evolutionary trees

Yusuke Matsui·Atsushi Niida·Ryutaro Uchi·Koshi Mimori·Satoru Miyano·Teppei Shimamura
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Abstract

MotivationMulti-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.\n\nResultsWe 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.\n\nAvailabilityphyC is implemented with R(>=3.2.2) and is available from https://github.com/ymatts/phyC.\n\nContactymatsui@med.nagoya-u.ac.jp

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Yusuke Matsui, Atsushi Niida, Ryutaro Uchi, Koshi Mimori, Satoru Miyano, Teppei Shimamura. 2016-08-12. phyC: Clustering cancer evolutionary trees. https://doi.org/10.1101/069302

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