phyC: Clustering cancer evolutionary trees

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

研究成果: Contribution to journalArticle査読

7 被引用数 (Scopus)


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

ジャーナルPLoS Computational Biology
出版ステータス出版済み - 5 2017

All Science Journal Classification (ASJC) codes

  • 生態、進化、行動および分類学
  • モデリングとシミュレーション
  • 生態学
  • 分子生物学
  • 遺伝学
  • 細胞および分子神経科学
  • 計算理論と計算数学


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