Dataset Information

Phylogenetic Tree Inference: A Top-Down Approach to Track Tumor Evolution.

ABSTRACT: Recently, an increasing number of studies sequence multiple biopsies of primary tumors, and even paired metastatic tumors to understand heterogeneity and the evolutionary trajectory of cancer progression. Although several algorithms are available to infer the phylogeny, most tools rely on accurate measurements of mutation allele frequencies from deep sequencing, which is often hard to achieve for clinical samples (especially FFPE samples). In this study, we present a novel and easy-to-use method, PTI (Phylogenetic Tree Inference), which use an iterative top-down approach to infer the phylogenetic tree structure of multiple tumor biopsies from same patient using just the presence or absence of somatic mutations without their allele frequencies. Therefore PTI can be used in a wide range of cases even when allele frequency data is not available. Comparison with existing state-of-the-art methods, such as LICHeE, Treeomics, and BAMSE, shows that PTI achieves similar or slightly better performance within a short run time. Moreover, this method is generally applicable to infer phylogeny for any other data sets (such as epigenetics) with a similar zero and one feature-by-sample matrix.

SUBMITTER: Wu P

PROVIDER: S-EPMC7020887 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

Phylogenetic Tree Inference: A Top-Down Approach to Track Tumor Evolution.

Wu Pin P Hou Linjun L Zhang Yingdong Y Zhang Liye L

Frontiers in genetics 20200207

Recently, an increasing number of studies sequence multiple biopsies of primary tumors, and even paired metastatic tumors to understand heterogeneity and the evolutionary trajectory of cancer progression. Although several algorithms are available to infer the phylogeny, most tools rely on accurate measurements of mutation allele frequencies from deep sequencing, which is often hard to achieve for clinical samples (especially FFPE samples). In this study, we present a novel and easy-to-use method ...[more]

PMID: 32117420

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Project description:Sampling across tree space is one of the major challenges in Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) algorithms. Standard MCMC tree moves consider small random perturbations of the topology, and select from candidate trees at random or based on the distance between the old and new topologies. MCMC algorithms using such moves tend to get trapped in tree space, making them slow in finding the globally most probable trees (known as "convergence") and in estimating the correct proportions of the different types of them (known as "mixing"). Here, we introduce a new class of moves, which propose trees based on their parsimony scores. The proposal distribution derived from the parsimony scores is a quickly computable albeit rough approximation of the conditional posterior distribution over candidate trees. We demonstrate with simulations that parsimony-guided moves correctly sample the uniform distribution of topologies from the prior. We then evaluate their performance against standard moves using six challenging empirical data sets, for which we were able to obtain accurate reference estimates of the posterior using long MCMC runs, a mix of topology proposals, and Metropolis coupling. On these data sets, ranging in size from 357 to 934 taxa and from 1740 to 5681 sites, we find that single chains using parsimony-guided moves usually converge an order of magnitude faster than chains using standard moves. They also exhibit better mixing, that is, they cover the most probable trees more quickly. Our results show that tree moves based on quick and dirty estimates of the posterior probability can significantly outperform standard moves. Future research will have to show to what extent the performance of such moves can be improved further by finding better ways of approximating the posterior probability, taking the trade-off between accuracy and speed into account. [Bayesian phylogenetic inference; MCMC; parsimony; tree proposal.].

Project description:The diversity of plant neighbors commonly results in direct, bottom-up effects on herbivore ability to locate their host, and in indirect effects on herbivores involving changes in plant traits and a top-down control by their enemies. Yet, the relative contribution of bottom-up and top-down forces remains poorly understood. We also lack knowledge on the effect of abiotic constraints such as summer drought on the strength and direction of these effects. We measured leaf damage on pedunculate oak (Quercus robur), alone or associated with birch, pine or both in a long-term tree diversity experiment (ORPHEE), where half of the plots were irrigated while the other half remained without irrigation and received only rainfall. We tested three mechanisms likely to explain the effects of oak neighbors on herbivory: (1) Direct bottom-up effects of heterospecific neighbors on oak accessibility to herbivores, (2) indirect bottom-up effects of neighbors on the expression of leaf traits, and (3) top-down control of herbivores by predators. Insect herbivory increased during the growth season but was independent of neighbor identity and irrigation. Specific leaf area, leaf toughness, and thickness varied with neighbor identity while leaf dry matter content or C:N ratio did not. When summarized in a principal component analysis (PCA), neighbor identity explained 87% of variability in leaf traits. PCA axes partially predicted herbivory. Despite greater rates of attack on dummy caterpillars in irrigated plots, avian predation, and insect herbivory remained unrelated. Our study suggests that neighbor identity can indirectly influence insect herbivory in mixed forests by modifying leaf traits. However, we found only partial evidence for these trait-mediated effects and suggest that more attention should be paid to some unmeasured plant traits such as secondary metabolites, including volatile organic compounds, to better anticipate the effects of climate change on plant-insect interactions in the future.

Dataset Information

Phylogenetic Tree Inference: A Top-Down Approach to Track Tumor Evolution.

Publications

Phylogenetic Tree Inference: A Top-Down Approach to Track Tumor Evolution.

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