Data simulation of tumor phylogenetic trees and evaluation of phylogenetic reconstructing tools

Abstract

Tumor heterogeneity describes that a tumor usually contains more than one type of cells which are called clones. Clones in a tumor have distinct morphological and physiological features such as genetic variations. Different clones display different sensitivities to cytotoxic drugs, and tumor heterogeneity can add complexity to understand tumor composition and pose challenges for the development of successful therapies. Thus, studying tumor heterogeneity can guide tumor therapies for individual patient and enhance our understanding of inter-clonal functional relationships during therapies, which could be benefit to personalized and efficient treatments.

Heterogenetic tumor development is an evolutionary process. There exists an evolutionary relationship among the clones of a heterogenetic tumor and the relationship can be described by an phylogenetic tree. Computational tools have been increasingly important to study tumor heterogeneity because of their time and economic efficiency. Such tools usually take as input the genetic variability data produced by high-throughput sequencing technologies, then output clonal composition of a tumor and reconstruct the polygenetic tree of it.

In this thesis, we simulated a large amount of datasets consisting of tumor phylogenetic trees with varying properties and used the datasets to evaluate five recent and popular tumor phylogenetic reconstructing computational tools. We found relatively large differences for performance among those tools and also their strengths and shortcomings, respectively. We left as future work improvement of the data simulation methods and exploration of tool parameters for possibly more beneficial results.