phyC: Clustering cancer evolutionary trees

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

Research output: Contribution to journalArticle

4 Citations (Scopus)

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.

Original languageEnglish
Article numbere1005509
JournalPLoS Computational Biology
Volume13
Issue number5
DOIs
Publication statusPublished - May 2017

Fingerprint

Evolutionary Tree
Cluster Analysis
Tumors
cancer
Cancer
Cells
Topology
Clustering
neoplasms
Sequencing
Neoplasms
Lung Cancer
Cell
Clustering Methods
Genetic Heterogeneity
Phenotype
lung neoplasms
kidney cells
Tumor
methodology

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Modelling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

Cite this

Matsui, Y., Niida, A., Uchi, R., Mimori, K., Miyano, S., & Shimamura, T. (2017). phyC: Clustering cancer evolutionary trees. PLoS Computational Biology, 13(5), [e1005509]. https://doi.org/10.1371/journal.pcbi.1005509

phyC : Clustering cancer evolutionary trees. / Matsui, Yusuke; Niida, Atsushi; Uchi, Ryutaro; Mimori, Koshi; Miyano, Satoru; Shimamura, Teppei.

In: PLoS Computational Biology, Vol. 13, No. 5, e1005509, 05.2017.

Research output: Contribution to journalArticle

Matsui, Y, Niida, A, Uchi, R, Mimori, K, Miyano, S & Shimamura, T 2017, 'phyC: Clustering cancer evolutionary trees', PLoS Computational Biology, vol. 13, no. 5, e1005509. https://doi.org/10.1371/journal.pcbi.1005509
Matsui, Yusuke ; Niida, Atsushi ; Uchi, Ryutaro ; Mimori, Koshi ; Miyano, Satoru ; Shimamura, Teppei. / phyC : Clustering cancer evolutionary trees. In: PLoS Computational Biology. 2017 ; Vol. 13, No. 5.
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