Apc(MIN) modulation of vitamin D secosteroid growth control

A central paradox of vitamin D biology is that 1alpha,25-(OH)(2) D(3) exposure inversely relates to colorectal cancer (CRC) risk despite a capacity for activation of both pro- and anti-oncogenic mediators including osteopontin (OPN)/CD44 and E-cadherin, respectively. Most sporadic CRCs arise from adenomatous polyposis coli (APC) gene mutation but understanding of its effects on vitamin D growth control is limited. Here we investigate effects of the Apc(Min/+) genotype on 1alpha,25-(OH)(2) D(3) regulation of OPN/CD44/E-cadherin signalling and intestinal tumourigenesis, in vivo. In untreated Apc(Min/+) versus Apc(+/+) intestines, expression levels of OPN and its CD44 receptor were increased, whereas E-cadherin tumour suppressor signalling was attenuated. Treatment by 1alpha,25-(OH)(2) D(3) or rationally designed analogues (QW or BTW) enhanced OPN but inhibited expression of CD44, the OPN receptor implicated in cell growth. These treatments also enhanced E-cadherin tumour suppressor activity, characterized by inhibition of beta-catenin nuclear localization, T-cell factor 1 and c-myelocytomatosis protein expression in Apc(Min/+) intestine. All secosteroids suppressed Apc(Min/+)-driven tumourigenesis although QW and BTW had lower calcium-related toxicity. Taken together, these data indicate that the Apc(Min/+) genotype modulates vitamin D secosteroid actions to promote functional predominance of E-cadherin tumour suppressor activity within antagonistic molecular networks. APC heterozygosity may promote favourable tissue- or tumour-specific conditions for growth control by vitamin D secosteroid treatment.

Min-BDeu and Max-BDeu Scores for Learning Bayesian Networks

This work presents two new score functions based on the Bayesian Dirichlet equivalent uniform (BDeu) score for learning Bayesian network structures. They consider the sensitivity of BDeu to varying parameters of the Dirichlet prior. The scores take on the most adversary and the most beneficial priors among those within a contamination set around the symmetric one. We build these scores in such way that they are decomposable and can be computed efficiently. Because of that, they can be integrated into any state-of-the-art structure learning method that explores the space of directed acyclic graphs and allows decomposable scores. Empirical results suggest that our scores outperform the standard BDeu score in terms of the likelihood of unseen data and in terms of edge discovery with respect to the true network, at least when the training sample size is small. We discuss the relation between these new scores and the accuracy of inferred models. Moreover, our new criteria can be used to identify the amount of data after which learning is saturated, that is, additional data are of little help to improve the resulting model.