COX-derived prostanoid pathways in gastrointestinal cancer development and progression : novel targets for prevention and intervention

Arachidonic acid metabolism through cyclooxygenase (COX) pathways leads to the generation of biologically active eicosanoids. Eicosanoid expression levels vary during development and progression of gastrointestinal (GI) malignancies. COX-2 is the major COX-isoform responsible for G.I. cancer development/progression. COX-2 expression increases during progression from a normal to cancerous state. Evidence from observational studies has demonstrated that chronic NSAID use reduces the risk of cancer development, while both incidence and risk of death due to G.I. cancers were significantly reduced by daily aspirin intake. A number of randomized controlled trials (APC trial, Prevention of Sporadic Adenomatous Polyps trial, APPROVe trial) have also shown a significant protective effect in patients receiving selective COX-2 inhibitors. However, chronic use of selective COX-2 inhibitors at high doses was associated with increased cardiovascular risk, while NSAIDs have also been associated with increased risk. More recently, downstream effectors of COX-signaling have been investigated in cancer development/progression. PGE 2, which binds to both EP and PPAR receptors, is the major prostanoid implicated in the carcinogenesis of G.I. cancers. The role of TXA 2 in G.I. cancers has also been examined, although further studies are required to uncover its role in carcinogenesis. Other prostanoids investigated include PGD 2 and its metabolite 15d-PGJ2, PGF 1α and PGI 2. Targeting these prostanoids in G.I. cancers has the promise of avoiding cardiovascular toxicity associated with chronic selective COX-2 inhibition, while maintaining anti-tumor reactivity.A progressive sequence from normal to pre-malignant to a malignant state has been identified in G.I. cancers. In this review, we will discuss the role of the COX-derived prostanoids in G.I. cancer development and progression. Targeting these downstream prostanoids for chemoprevention and/or treatment of G.I. cancers will also be discussed. Finally, we will highlight the latest pre-clinical technologies as well as avenues for future investigation in this highly topical research field. © 2011 Elsevier B.V.

Visualization of predictive distributions for discrete spatial-temporal log Cox processes approximated with MCMC

An important aspect of decision support systems involves applying sophisticated and flexible statistical models to real datasets and communicating these results to decision makers in interpretable ways. An important class of problem is the modelling of incidence such as fire, disease etc. Models of incidence known as point processes or Cox processes are particularly challenging as they are ‘doubly stochastic’ i.e. obtaining the probability mass function of incidents requires two integrals to be evaluated. Existing approaches to the problem either use simple models that obtain predictions using plug-in point estimates and do not distinguish between Cox processes and density estimation but do use sophisticated 3D visualization for interpretation. Alternatively other work employs sophisticated non-parametric Bayesian Cox process models, but do not use visualization to render interpretable complex spatial temporal forecasts. The contribution here is to fill this gap by inferring predictive distributions of Gaussian-log Cox processes and rendering them using state of the art 3D visualization techniques. This requires performing inference on an approximation of the model on a discretized grid of large scale and adapting an existing spatial-diurnal kernel to the log Gaussian Cox process context.