20,987 research outputs found
A Mutagenetic Tree Hidden Markov Model for Longitudinal Clonal HIV Sequence Data
RNA viruses provide prominent examples of measurably evolving populations. In
HIV infection, the development of drug resistance is of particular interest,
because precise predictions of the outcome of this evolutionary process are a
prerequisite for the rational design of antiretroviral treatment protocols. We
present a mutagenetic tree hidden Markov model for the analysis of longitudinal
clonal sequence data. Using HIV mutation data from clinical trials, we estimate
the order and rate of occurrence of seven amino acid changes that are
associated with resistance to the reverse transcriptase inhibitor efavirenz.Comment: 20 pages, 6 figure
Every which way? On predicting tumor evolution using cancer progression models
Successful prediction of the likely paths of tumor progression is valuable for diagnostic,
prognostic, and treatment purposes. Cancer progression models (CPMs) use cross-sectional samples to identify restrictions in the order of accumulation of driver mutations and
thus CPMs encode the paths of tumor progression. Here we analyze the performance of
four CPMs to examine whether they can be used to predict the true distribution of paths of
tumor progression and to estimate evolutionary unpredictability. Employing simulations we
show that if fitness landscapes are single peaked (have a single fitness maximum) there is
good agreement between true and predicted distributions of paths of tumor progression
when sample sizes are large, but performance is poor with the currently common much
smaller sample sizes. Under multi-peaked fitness landscapes (i.e., those with multiple fitness maxima), performance is poor and improves only slightly with sample size. In all
cases, detection regime (when tumors are sampled) is a key determinant of performance.
Estimates of evolutionary unpredictability from the best performing CPM, among the four
examined, tend to overestimate the true unpredictability and the bias is affected by detection
regime; CPMs could be useful for estimating upper bounds to the true evolutionary unpredictability. Analysis of twenty-two cancer data sets shows low evolutionary unpredictability
for several of the data sets. But most of the predictions of paths of tumor progression are
very unreliable, and unreliability increases with the number of features analyzed. Our results
indicate that CPMs could be valuable tools for predicting cancer progression but that, currently, obtaining useful predictions of paths of tumor progression from CPMs is dubious, and
emphasize the need for methodological work that can account for the probably multi-peaked
fitness landscapes in cancerWork partially supported by BFU2015-
67302-R (MINECO/FEDER, EU) to RDU. CV
supported by PEJD-2016-BMD-2116 from
Comunidad de Madrid to RD
Phylogenetic surveillance of viral genetic diversity and the evolving molecular epidemiology of human immunodeficiency virus type 1
With ongoing generation of viral genetic diversity and increasing levels of migration, the global human immunodeficiency virus type 1 (HIV-1) epidemic is becoming increasingly heterogeneous. In this study, we investigate the epidemiological characteristics of 5,675 HIV-1 pol gene sequences sampled from distinct infections in the United Kingdom. These sequences were phylogenetically analyzed in conjunction with 976 complete-genome and 3,201 pol gene reference sequences sampled globally and representing the broad range of HIV-1 genetic diversity, allowing us to estimate the probable geographic origins of the various strains present in the United Kingdom. A statistical analysis of phylogenetic clustering in this data set identified several independent transmission chains within the United Kingdom involving recently introduced strains and indicated that strains more commonly associated with infections acquired heterosexually in East Africa are spreading among men who have sex with men. Coalescent approaches were also used and indicated that the transmission chains that we identify originated in the late 1980s to early 1990s. Similar changes in the epidemiological structuring of HIV epidemics are likely to be taking in place in other industrialized nations with large immigrant populations. The framework implemented here takes advantage of the vast amount of routinely generated HIV-1 sequence data and can provide epidemiological insights not readily obtainable through standard surveillance methods
Computational Cancer Biology: An Evolutionary Perspective
ISSN:1553-734XISSN:1553-735
An immune network approach to learning qualitative models of biological pathways
ACKNOWLEDGMENT GMC is supported by the CRISP project (Combinatorial Responses In Stress Pathways) funded by the BBSRC (BB/F00513X/1) under the Systems Approaches to Biological Research (SABR) Initiative. WP and GMC are also supported by the partnership fund from dot.rural, RCUK Digital Economy research.Postprin
HyperTraPS: Inferring probabilistic patterns of trait acquisition in evolutionary and disease progression pathways
The explosion of data throughout the biomedical sciences provides unprecedented opportunities to learn about the dynamics of evolution and disease progression, but harnessing these large and diverse datasets remains challenging. Here, we describe a highly generalisable statistical platform to infer the dynamic pathways by which many, potentially interacting, discrete traits are acquired or lost over time in biomedical systems. The platform uses HyperTraPS (hypercubic transition path sampling) to learn progression pathways from cross-sectional, longitudinal, or phylogenetically-linked data with unprecedented efficiency, readily distinguishing multiple competing pathways, and identifying the most parsimonious mechanisms underlying given observations. Its Bayesian structure quantifies uncertainty in pathway structure and allows interpretable predictions of behaviours, such as which symptom a patient will acquire next. We exploit the model’s topology to provide visualisation tools for intuitive assessment of multiple, variable pathways. We apply the method to ovarian cancer progression and the evolution of multidrug resistance in tuberculosis, demonstrating its power to reveal previously undetected dynamic pathways
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