Analysis of endogenous retinoic acid receptor expression and function in murine melanoma cells

There have been numerous reports over the past several years on the ability of vitamin A analogs (retinoids) to modulate cell proliferation, malignant transformation, morphogenesis, and differentiation in a wide variety of cell types and organisms. Two families of nuclear retinoid-inducible, trans-acting, transcription-enhancing receptors that bear strong DNA sequence homology to thyroid and steroid hormone receptors have recently been discovered. The retinoic acid receptors (RARs) and retinoid X receptors (RXRs) each have at least three types designated $\alpha,$ $\beta,$ and $\gamma,$ which are encoded by separate genes and expressed in a tissue and cell type-specific manner. We have been interested in the mechanism by which retinoids inhibit tumor cell proliferation and induce differentiation. As a model system we have employed several murine melanoma cell lines (S91-C2, K1735P, and B16-F1), which are sensitive to the growth-inhibitory and differentiation-inducing effects of RA, as well as a RA-resistant subclone of one of the cell lines (S91-C154), in order to study the role of the nuclear RARs in these effects. The initial phase of this project consisted of the characterization of the expression pattern of the three known RAR and RXR types in the murine melanoma cell lines in order to determine whether any differences exist which may elucidate a role for any of the receptors in RA-induced growth inhibition and differentiation. The novel finding was made that the RAR-$\beta$ gene is rapidly induced from undetectable levels by RA treatment at the mRNA and protein level, and that the induction of RAR-$\beta$ by other biologically active retinoids correlated with their ability to inhibit the growth of the highly RA-sensitive S91-C2 cell line. This suggests a role for RAR-$\beta$ in the growth inhibiting effect of retinoids. The second phase of this project involves the stable expression of RAR-$\beta$ in the S91-C2 cells and the RAR-$\beta$ receptor-null cell line, K1735P. These studies have indicated an inverse correlation between RAR-$\beta$ expression and proliferation rate. ^

Bayesian generalized linear models for meta-analysis of diagnostic tests

With the recognition of the importance of evidence-based medicine, there is an emerging need for methods to systematically synthesize available data. Specifically, methods to provide accurate estimates of test characteristics for diagnostic tests are needed to help physicians make better clinical decisions. To provide more flexible approaches for meta-analysis of diagnostic tests, we developed three Bayesian generalized linear models. Two of these models, a bivariate normal and a binomial model, analyzed pairs of sensitivity and specificity values while incorporating the correlation between these two outcome variables. Noninformative independent uniform priors were used for the variance of sensitivity, specificity and correlation. We also applied an inverse Wishart prior to check the sensitivity of the results. The third model was a multinomial model where the test results were modeled as multinomial random variables. All three models can include specific imaging techniques as covariates in order to compare performance. Vague normal priors were assigned to the coefficients of the covariates. The computations were carried out using the 'Bayesian inference using Gibbs sampling' implementation of Markov chain Monte Carlo techniques. We investigated the properties of the three proposed models through extensive simulation studies. We also applied these models to a previously published meta-analysis dataset on cervical cancer as well as to an unpublished melanoma dataset. In general, our findings show that the point estimates of sensitivity and specificity were consistent among Bayesian and frequentist bivariate normal and binomial models. However, in the simulation studies, the estimates of the correlation coefficient from Bayesian bivariate models are not as good as those obtained from frequentist estimation regardless of which prior distribution was used for the covariance matrix. The Bayesian multinomial model consistently underestimated the sensitivity and specificity regardless of the sample size and correlation coefficient. In conclusion, the Bayesian bivariate binomial model provides the most flexible framework for future applications because of its following strengths: (1) it facilitates direct comparison between different tests; (2) it captures the variability in both sensitivity and specificity simultaneously as well as the intercorrelation between the two; and (3) it can be directly applied to sparse data without ad hoc correction. ^

Nonlinear dimension reduction in pathway-based genome-wide association analysis

Pathway based genome wide association study evolves from pathway analysis for microarray gene expression and is under rapid development as a complementary for single-SNP based genome wide association study. However, it faces new challenges, such as the summarization of SNP statistics to pathway statistics. The current study applies the ridge regularized Kernel Sliced Inverse Regression (KSIR) to achieve dimension reduction and compared this method to the other two widely used methods, the minimal-p-value (minP) approach of assigning the best test statistics of all SNPs in each pathway as the statistics of the pathway and the principal component analysis (PCA) method of utilizing PCA to calculate the principal components of each pathway. Comparison of the three methods using simulated datasets consisting of 500 cases, 500 controls and100 SNPs demonstrated that KSIR method outperformed the other two methods in terms of causal pathway ranking and the statistical power. PCA method showed similar performance as the minP method. KSIR method also showed a better performance over the other two methods in analyzing a real dataset, the WTCCC Ulcerative Colitis dataset consisting of 1762 cases, 3773 controls as the discovery cohort and 591 cases, 1639 controls as the replication cohort. Several immune and non-immune pathways relevant to ulcerative colitis were identified by these methods. Results from the current study provided a reference for further methodology development and identified novel pathways that may be of importance to the development of ulcerative colitis.^