PhyloOncology: Understanding cancer through phylogenetic analysis

Despite decades of research and an enormity of resultant data, cancer remains a significant public health problem. New tools and fresh perspectives are needed to obtain fundamental insights, to develop better prognostic and predictive tools, and to identify improved therapeutic interventions. With increasingly common genome-scale data, one suite of algorithms and concepts with potential to shed light on cancer biology is phylogenetics, a scientific discipline used in diverse fields. From grouping subsets of cancer samples to tracing subclonal evolution during cancer progression and metastasis, the use of phylogenetics is a powerful systems biology approach. Well-developed phylogenetic applications provide fast, robust approaches to analyze high-dimensional, heterogeneous cancer data sets. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

The mega-matrix tree of life: using genome-scale horizontal gene transfer and sequence evolution data as information about the vertical history of life

Because horizontal gene transfer can confound the recovery of the largely prokaryotic tree of life (ToL), most genome-based techniques seek to eliminate horizontal signal from ToL analyses, commonly by sieving out incongruent genes and data. This approach greatly limits the number of gene families analysed to a subset thought to be representative of vertical evolutionary history. However, formalized tests have not been performed to determine whether combining the massive amounts of information available in fully sequenced genomes can recover a reasonable ToL. Consequently, we used empirically defined gene homology definitions from a previous study that delineate xenologous gene families (gene families derived from a common transfer event) to generate a massively concatenated, combined-data ToL matrix derived from 323 404 translated open reading frames arranged into 12 381 gene homologue groups coded as amino acid data and 63 336, 64 105, 65 153, 66 922 and 67 109 gene homologue groups coded as gene presence/absence data for 166 fully sequenced genomes. This whole-genome gene presence/absence and amino acid sequence ToL data matrix is composed of 4867 184 characters (a combined data-type mega-matrix). Phylogenetic analysis of this mega-matrix yielded a fully resolved ToL that classifies all three commonly accepted domains of life as monophyletic and groups most taxa in traditionally recognized locations with high support. Most importantly, these results corroborate the existence of a common evolutionary history for these taxa present in both data types that is evident only when these data are analysed in combination. © The Willi Hennig Society 2010.

Inferring evolutionary trees with PAUP*

This unit provides a general description of reconstructing evolutionary trees using PAUP* 4.0. The protocol takes users through an example analysis of mitochondrial DNA sequence data using the parsimony and the likelihood criteria to infer optimal trees. The protocol also discusses searching options available in PAUP* and demonstrates how to import non-NEXUS formats. Finally, a general discussion is given regarding the pros and cons of the "model-free" and "model-based" methods used throughout the protocol.

A compound poisson process for relaxing the molecular clock

The molecular clock hypothesis remains an important conceptual and analytical tool in evolutionary biology despite the repeated observation that the clock hypothesis does not perfectly explain observed DNA sequence variation. We introduce a parametric model that relaxes the molecular clock by allowing rates to vary across lineages according to a compound Poisson process. Events of substitution rate change are placed onto a phylogenetic tree according to a Poisson process. When an event of substitution rate change occurs, the current rate of substitution is modified by a gamma-distributed random variable. Parameters of the model can be estimated using Bayesian inference. We use Markov chain Monte Carlo integration to evaluate the posterior probability distribution because the posterior probability involves high dimensional integrals and summations. Specifically, we use the Metropolis-Hastings-Green algorithm with 11 different move types to evaluate the posterior distribution. We demonstrate the method by analyzing a complete mtDNA sequence data set from 23 mammals. The model presented here has several potential advantages over other models that have been proposed to relax the clock because it is parametric and does not assume that rates change only at speciation events. This model should prove useful for estimating divergence times when substitution rates vary across lineages.