Tumor phylogenetics in the NGS era: Strategies, challenges, and future prospects

Abstract

Tumor phylogenetics is a strategy for interpreting the evolution of tumors using computer algorithms for phylogenetics, i.e., the inference of evolutionary trees. The approach takes advantage of a large body of phylogenetic theory and algorithms, developed primarily for inferring evolution among species, to interpret complex tumor data sets as evidence for evolutionary processes. The result is a tumor phylogeny, or phylogenetic tree, a reconstruction of the sequences of mutations that cells within a tumor or class of tumors accumulate over the course of their progression. The goals of finding such trees are to better interpret heterogeneity within and among tumors, identify and classify tumor subtypes, learn markers of progression for key steps in tumor evolution, and enable predictive modeling of likely tumor progression steps that may ultimately assist in diagnosis and treatment. With the rise of whole-genome sequencing data, the need for sophisticated models and algorithms that can make sense of such data has never been more acute. In this chapter, we cover the fundamentals of reconstructing tumor phylogenies with a special focus on next-generation sequencing data and discuss recent research, current trends, and challenges and opportunities currently facing the field.