PhyC: Clustering cancer evolutionary trees.

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

doi:10.1371/journal.pcbi.1005509

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.

Highlights

Computational methods have been extensively studied to reconstruct a cancer evolutionary pattern within a patient, which is visualized as a socalled “cancer evolutionary tree” constructed from multi-regional sequencing data
We effectively identified the patterns of different evolutionary modes in a simulation analysis, and successfully detected the phenotype-related and cancer type-related subgroups to characterize tree structures within subgroups using actual datasets
We propose a new clustering method for cancer evolutionary trees based on tree topologies and edge attributes that describe the relationships of sub-clones and the number of somatic single nucleotide variant (SSNV) that accumulate in the sub-clones

Summary

Introduction

The high genetic diversity is driven by several evolutionary processes such as somatic mutation, genetic drift, migration, and natural selection. The clonal theory of cancer [1] is based on Darwinian models of natural selection in which genetically unstable cells acquire a somatic single nucleotide variant (SSNV), and selective pressure results in tumors with a biological fitness advantage for survival. The development of multi-regional sequencing techniques has provided new perspectives of genetic heterogeneity within or between common tumors [2,3,4,5,6]. The read counts from multi-region tumor and matched normal tissue sequences from each patient are used to infer the tumor composition and evolutionary structure from variant allele frequencies (VAFs); i.e., the proportion of reads containing the variant allele. Using the VAF, the cancer evolutionary histories can be reconstructed as a tree, termed a cancer evolutionary tree, which reflects the accumulation patterns of the identified SSNVs for each patient

Methods

Results

Discussion

Conclusion