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phyC: Clustering cancer evolutionary trees.

Yusuke Matsui1, Atsushi Niida2, Ryutaro Uchi3

  • 1Division of Systems Biology, Nagoya University Graduate School of Medicine, Nagoya, Japan.

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|May 2, 2017
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Summary
This summary is machine-generated.

This study introduces phyC, a novel clustering method for cancer evolutionary trees, aiding in the analysis of tumor heterogeneity and sub-clonal expansion. The method effectively identifies distinct evolutionary patterns in cancer types like renal and lung cancer.

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Area of Science:

  • * Computational Biology
  • * Evolutionary Biology
  • * Oncology

Background:

  • * Multi-regional sequencing enables investigation of genetic heterogeneity in tumors from an evolutionary standpoint.
  • * Existing methods focus on reconstructing single cancer evolutionary trees, with limited comparative studies.
  • * Understanding cancer evolution requires comparative analysis of multiple evolutionary trees.

Purpose of the Study:

  • * To propose a clustering method (phyC) for grouping cancer evolutionary trees based on topological and edge length attributes.
  • * To develop a complementary method for assessing sub-clonal diversity within identified clusters, offering insights into sub-clonal expansion dynamics.
  • * To provide a tool for comparative analysis of cancer evolution across different tumor types and phenotypes.

Main Methods:

  • * Development of the phyC clustering algorithm utilizing tree topology and edge length.
  • * Implementation of a sub-clonal diversity evaluation metric for cluster interpretation.
  • * Validation through simulations to assess clustering accuracy.
  • * Application to multi-regional sequencing data from clear cell renal carcinoma and non-small cell lung cancer.

Main Results:

  • * Simulations demonstrated that phyC accurately detects true clusters in cancer evolutionary trees.
  • * Application to real-world cancer data identified clusters associated with specific cancer types (renal, lung) and phenotypes.
  • * The method successfully revealed patterns related to sub-clonal diversity and expansion acceleration.

Conclusions:

  • * phyC offers a robust approach for clustering cancer evolutionary trees, facilitating comparative evolutionary analyses.
  • * The method enhances understanding of tumor heterogeneity and sub-clonal dynamics.
  • * phyC provides valuable insights into cancer evolution, applicable to diverse cancer types and phenotypes.