Abstract

BackgoundEvolution of cancer cells is characterized by large scale and rapid changes in the chromosomal  landscape. The fluorescence in situ hybridization (FISH) technique provides a way to measure the copy numbers of preselected genes in a group of cells and has been found to be a reliable source of data to model the evolution of tumor cells. Chowdhury et al. (Bioinformatics 29(13):189–98, 23; PLoS Comput Biol 10(7):1003740, 24) recently develop a computational model for tumor progression driven by gains and losses in cell count patterns obtained by FISH probes. Their model aims to find the rectilinear Steiner minimum tree (RSMT) (Chowdhury et al. in Bioinformatics 29(13):189–98, 23) and the duplication Steiner minimum tree (DSMT) (Chowdhury et al. in PLoS Comput Biol 10(7):1003740, 24) that describe the progression of FISH cell count patterns over its branches in a parsimonious manner. Both the RSMT and DSMT problems are NP-hard and heuristics are required to solve the problems efficiently.MethodsIn this paper we propose two approaches to solve the RSMT problem, one inspired by iterative methods to address the “small phylogeny” problem (Sankoff et al. in J Mol Evol 7(2):133–49, 27; Blanchette et al. in Genome Inform 8:25–34, 28), and the other based on maximum parsimony phylogeny inference. We further show how to extend these heuristics to obtain solutions to the DSMT problem, that models large scale duplication events.ResultsExperimental results from both simulated and real tumor data show that our methods outperform previous heuristics (Chowdhury et al. in Bioinformatics 29(13):189–98, 23; Chowdhury et al. in PLoS Comput Biol 10(7):1003740, 24) in obtaining solutions to both RSMT and DSMT problems.ConclusionThe methods introduced here are able to provide more parsimony phylogenies compared to earlier ones which are consider better choices.

Highlights

  • Cancer is recognized to be an evolutionary process driven by mutations in tumor cells [1]

  • Experimental results from both simulated and real tumor data show that our methods outperform previous heuristics (Chowdhury et al in Bioinformatics 29(13):189–98, 23; Chowdhury et al in PLoS Comput Biol 10(7):1003740, 24) in obtaining solutions to both rectilinear Steiner minimum tree (RSMT) and duplication Steiner minimum tree (DSMT) problems

  • Methods we describe the rectilinear Steiner minimum tree (RSMT) and the duplication Steiner minimum tree (DSMT) problems for modeling the progression of fluorescence in situ hybridization (FISH) cell count patterns and compare them with minimum spanning tree (MST) and maximum parsimony tree (MPT) problems

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Summary

Introduction

Cancer is recognized to be an evolutionary process driven by mutations in tumor cells [1]. Many experiments reveal considerable intra-tumor and intertumor heterogeneity [3], attributed to these evolutionary processes. Simultaneous linear and branching evolution in multiple subclones of cancer cells can be modeled by a phylogenetic tree [5]. Inferring such phylogenies facilitates the study of cancer initiation, progression, treatment, and resistance [6]. They can help pinpoint important changes that lead to the recurrence of some genome aberrations [7]. Phylogeny studies aid in identifying genes crucial for evolution and may contribute to developing better cancer treatment [8,9,10,11]

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