[Show abstract][Hide abstract] ABSTRACT: Although lactic acidosis is a prominent feature of solid tumors, we still have limited understanding of the mechanisms by which lactic acidosis influences metabolic phenotypes of cancer cells. We compared global transcriptional responses of breast cancer cells in response to three distinct tumor microenvironmental stresses: lactic acidosis, glucose deprivation, and hypoxia. We found that lactic acidosis and glucose deprivation trigger highly similar transcriptional responses, each inducing features of starvation response. In contrast to their comparable effects on gene expression, lactic acidosis and glucose deprivation have opposing effects on glucose uptake. This divergence of metabolic responses in the context of highly similar transcriptional responses allows the identification of a small subset of genes that are regulated in opposite directions by these two conditions. Among these selected genes, TXNIP and its paralogue ARRDC4 are both induced under lactic acidosis and repressed with glucose deprivation. This induction of TXNIP under lactic acidosis is caused by the activation of the glucose-sensing helix-loop-helix transcriptional complex MondoA:Mlx, which is usually triggered upon glucose exposure. Therefore, the upregulation of TXNIP significantly contributes to inhibition of tumor glycolytic phenotypes under lactic acidosis. Expression levels of TXNIP and ARRDC4 in human cancers are also highly correlated with predicted lactic acidosis pathway activities and associated with favorable clinical outcomes. Lactic acidosis triggers features of starvation response while activating the glucose-sensing MondoA-TXNIP pathways and contributing to the "anti-Warburg" metabolic effects and anti-tumor properties of cancer cells. These results stem from integrative analysis of transcriptome and metabolic response data under various tumor microenvironmental stresses and open new paths to explore how these stresses influence phenotypic and metabolic adaptations in human cancers.
[Show abstract][Hide abstract] ABSTRACT: We discuss Bayesian modelling and computational methods in analysis of indirectly observed spatial point processes. The context involves noisy measurements on an underlying point process that provide indirect and noisy data on locations of point outcomes. We are interested in problems in which the spatial intensity function may be highly heterogenous, and so is modelled via flexible nonparametric Bayesian mixture models. Analysis aims to estimate the underlying intensity function and the abundance of realized but unobserved points. Our motivating applications involve immunological studies of multiple fluorescent intensity images in sections of lymphatic tissue where the point processes represent geographical configurations of cells. We are interested in estimating intensity functions and cell abundance for each of a series of such data sets to facilitate comparisons of outcomes at different times and with respect to differing experimental conditions. The analysis is heavily computational, utilizing recently introduced MCMC approaches for spatial point process mixtures and extending them to the broader new context here of unobserved outcomes. Further, our example applications are problems in which the individual objects of interest are not simply points, but rather small groups of pixels; this implies a need to work at an aggregate pixel region level and we develop the resulting novel methodology for this. Two examples with with immunofluorescence histology data demonstrate the models and computational methodology.
[Show abstract][Hide abstract] ABSTRACT: Epidemiological interventions aim to control the spread of infectious disease through various mechanisms, each carrying a different associated cost.
We describe a flexible statistical framework for generating optimal epidemiological interventions that are designed to minimize the total expected cost of an emerging epidemic while simultaneously propagating uncertainty regarding the underlying disease model parameters through to the decision process. The strategies produced through this framework are adaptive: vaccination schedules are iteratively adjusted to reflect the anticipated trajectory of the epidemic given the current population state and updated parameter estimates.
Using simulation studies based on a classic influenza outbreak, we demonstrate the advantages of adaptive interventions over non-adaptive ones, in terms of cost and resource efficiency, and robustness to model misspecification.
PLoS ONE 02/2009; 4(6):e5807. · 3.53 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: We present an applied study in cancer genomics for integrating data and inferences from laboratory experiments on cancer cell lines with observational data obtained from human breast cancer studies. The biological focus is on improving understanding of transcriptional responses of tumors to changes in the pH level of the cellular microenvironment. The statistical focus is on connecting experimentally defined biomarkers of such responses to clinical outcome in observational studies of breast cancer patients. Our analysis exemplifies a general strategy for accomplishing this kind of integration across contexts. The statistical methodologies employed here draw heavily on Bayesian sparse factor models for identifying, modularizing and correlating with clinical outcome these signatures of aggregate changes in gene expression. By projecting patterns of biological response linked to specific experimental interventions into observational studies where such responses may be evidenced via variation in gene expression across samples, we are able to define biomarkers of clinically relevant physiological states and outcomes that are rooted in the biology of the original experiment. Through this approach we identify microenvironment-related prognostic factors capable of predicting long term survival in two independent breast cancer datasets. These results suggest possible directions for future laboratory studies, as well as indicate the potential for therapeutic advances though targeted disruption of specific pathway components.
The Annals of Applied Statistics 01/2009; 3(4):1675-1694. · 2.24 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: We present a statistical approach for indentifying residues in DNA sequences for which diversity may be maintained by natural selection. Bayesian generalized linear models (GLMs) are used to describe patterns of mutation in a DNA sequence alignment. Posterior distributions of key quantities, such as probabilities of nonsynonymous and synonymous mutation per site, are studied. Inference in this class of models is achived through customary Markov chain Monte Carlo methods. Model selection is dealt with by means of a minimum posterior predictive loss approach. We describe how information on the evolutionary process underlying the sequences can be formally incorporated into the models through structured priors. The proposed methodology was designed to analyze several DNA sequences encoding the vaccine candidate apical membrane antigen-1 (AMA-1) of the human malaria parasite plasmodium falciparum. The study of genetic variability in antigen sequences is relevant to determining whether a particular antigen is a viable target for a vaccine construct. Using a simulation study, we first compare the GLM-based approach to existing methods for detecting sites under selection that are based on stochastic models of sequence evolution. We then apply the proposed models to the AMA-1 sequence data, which allows us to identify residues with the greatest disparities between nonsynonymous and synonymous changes. Recent experimental evidence suggests that several of these residues are immunologically relevant, indicating that the proposed models may be used predictively to identify functionally significant residues in antigens for which experimental results are not yet available.
Journal of the American Statistical Association 01/2008; 103(484):1496-1507. · 1.83 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Recent developments in Bayesian modelling of DNA sequence data for de-tecting natural selection at the amino acid level are presented. This article summarizes and discusses empirical model-based approaches. Key features of the modelling framework include the incorporation of biologically meaningful information via structured priors, posterior detection of sites under selection, and model validation via posterior predictive checks and/or estimation of gene and species trees. In addition, model selection is handled using a minimum posterior predictive loss criterion. The models presented here can incorpo-rate relevant covariates such as amino acid properties, extending in this way previous approaches. Applications include the analysis of two DNA sequence alignments with different characteristics in terms of evolutionary divergences among the sequences: an abalone sperm lysin alignment with a strong underly-ing phylogenetic structure and a low divergence sequence alignment encoding the Apical Membrane Antigen-1 (AMA-1) in the P.falciparum human malaria parasite.
[Show abstract][Hide abstract] ABSTRACT: Recent developments in Bayesian modelling of DNA sequence data for de-tecting natural selection at the amino acid level are presented. This article summarizes and discusses empirical model-based approaches. Key features of the modelling framework include the incorporation of biologically meaningful information via structured priors, posterior detection of sites under selection, and model validation via posterior predictive checks and/or estimation of gene and species trees. In addition, model selection is handled using a minimum posterior predictive loss criterion. The models presented here can incorpo-rate relevant covariates such as amino acid properties, extending in this way previous approaches. Applications include the analysis of two DNA sequence alignments with different characteristics in terms of evolutionary divergences among the sequences: an abalone sperm lysin alignment with a strong underly-ing phylogenetic structure and a low divergence sequence alignment encoding the Apical Membrane Antigen-1 (AMA-1) in the human P.falciparum malaria parasite.
World Meeting on Bayesian Statistics Benidorm. 07/2006;
[Show abstract][Hide abstract] ABSTRACT: Abstract From a variety of vantage points, ranging from epidemiological to statistical, the problem of identifying the eects of natural selection at the molecular level is a fascinat- ing one. Recent years have seen an explosion of model based methods for inferring such eects, with particular emphasis on detection of positive selection; some of the most popular of which are the maximum,likelihood based method of Yang implemented in PAML, the parsimony based method of Suzuki and Gojorobi implemented in ADAPT- SITE, and the hierarchical Bayesian method of Huelsenbeck and Ronquist implemented in MRBAYES. Although each of these three methodologies has appeared in the liter- ature in the analyses of various sequence data, there have been no cross comparison studies of the performance of these methods when applied to the same data, in terms
[Show abstract][Hide abstract] ABSTRACT: A positively selected amino acid site is one for which natural selection encourages diversification. The identification of such sites is of biomedical impor- tance, as diversifying sites cannot act as reliable binding sites for location-specific drugs. We in- troduce a new method for detecting positive selec- tion based on a class of Bayesian generalized lin- ear models (GLMs). This method does not re- quire explicit assumptions about phylogeny and of- fers relatively reduced time to Markov chain Monte Carlo (MCMC) convergence. We compare our Bayesian GLM approach with three current methods for detecting positive selection: Nei and Gojobori's ADAPTSITE, Yang's PAML, and Huelsenbeck and Ron- quist's MrBayes.