Differential gene expression in multistep tumorigenesis using an {\it in vitro\/} human cell model

Using a human terato-carcinoma cell line, PA-1, the functional role of the oncogenes and tumor suppressor gene involved in the multistep process of carcinogenesis have been analyzed. The expression of AP-2 was strongly correlated with the susceptibility to ras transformation. The differential responsiveness to growth factors between stage 1 ras resistant cells and stage 2 ras susceptible cells was observed, indicating that the ability of stage 2 cells to respond to the mutated ras oncogenes in transformation correlated with the ability to be stimulated by certain growth factors. Using differential screening of cDNA libraries, a number of differentially expressed cDNA clones was isolated. One of those, clone 12, is overexpressed in ras transformed stage 3 cells. The amino acid sequence of clone 12 is almost identical to a mouse LLrep3 gene that was growth-regulated, and 78% similar to a yeast ribosomal protein S4. These results suggest that the S4 gene may be involved in regulation of growth. Clone 9 is expressed in stage 1 ras resistant cells (3.5-kb and 3.0-kb transcripts) but the expression of this clone in stage 2 ras susceptible cells and stage 3 ras-transformed cells is greatly diminished. The expression of this cDNA clone was increased to at least five fold in ras resistant cells and nontumorigenic hybrids treated with retinoic acid but not increased in retinoic acid treated ras susceptible cells, ras transformed cells and the tumorigenic segregants. Partial sequence of this clone showed no homology to the sequences in Genbank. These findings suggest that clone 9 could be a suppressor gene or the genes that are involved in the biochemical pathway of tumor suppression or neurogenic differentiation. The apparent pleiotropic effect of the loss of this suppressor gene function support Harris' proposal that tumor suppressor genes regulate differentiation. The tumor suppressor gene may act as negative regulator of tumor growth by controlling gene expression in differentiation. ^

A nonparametric method of comparing the partial areas under two correlated receiver operating characteristic curves

A non-parametric method was developed and tested to compare the partial areas under two correlated Receiver Operating Characteristic curves. Based on the theory of generalized U-statistics the mathematical formulas have been derived for computing ROC area, and the variance and covariance between the portions of two ROC curves. A practical SAS application also has been developed to facilitate the calculations. The accuracy of the non-parametric method was evaluated by comparing it to other methods. By applying our method to the data from a published ROC analysis of CT image, our results are very close to theirs. A hypothetical example was used to demonstrate the effects of two crossed ROC curves. The two ROC areas are the same. However each portion of the area between two ROC curves were found to be significantly different by the partial ROC curve analysis. For computation of ROC curves with large scales, such as a logistic regression model, we applied our method to the breast cancer study with Medicare claims data. It yielded the same ROC area computation as the SAS Logistic procedure. Our method also provides an alternative to the global summary of ROC area comparison by directly comparing the true-positive rates for two regression models and by determining the range of false-positive values where the models differ. ^

An ordinal logistic regression model with misclassification of the outcome variable and categorical covariate

Ordinal outcomes are frequently employed in diagnosis and clinical trials. Clinical trials of Alzheimer's disease (AD) treatments are a case in point using the status of mild, moderate or severe disease as outcome measures. As in many other outcome oriented studies, the disease status may be misclassified. This study estimates the extent of misclassification in an ordinal outcome such as disease status. Also, this study estimates the extent of misclassification of a predictor variable such as genotype status. An ordinal logistic regression model is commonly used to model the relationship between disease status, the effect of treatment, and other predictive factors. A simulation study was done. First, data based on a set of hypothetical parameters and hypothetical rates of misclassification was created. Next, the maximum likelihood method was employed to generate likelihood equations accounting for misclassification. The Nelder-Mead Simplex method was used to solve for the misclassification and model parameters. Finally, this method was applied to an AD dataset to detect the amount of misclassification present. The estimates of the ordinal regression model parameters were close to the hypothetical parameters. β1 was hypothesized at 0.50 and the mean estimate was 0.488, β2 was hypothesized at 0.04 and the mean of the estimates was 0.04. Although the estimates for the rates of misclassification of X1 were not as close as β1 and β2, they validate this method. X 1 0-1 misclassification was hypothesized as 2.98% and the mean of the simulated estimates was 1.54% and, in the best case, the misclassification of k from high to medium was hypothesized at 4.87% and had a sample mean of 3.62%. In the AD dataset, the estimate for the odds ratio of X 1 of having both copies of the APOE 4 allele changed from an estimate of 1.377 to an estimate 1.418, demonstrating that the estimates of the odds ratio changed when the analysis includes adjustment for misclassification. ^

Adenosine induced pulmonary fibrosis and the contribution of the adenosine A2B receptor to the induction of osteopontin in a mouse model of pulmonary fibrosis

Pulmonary fibrosis is a devastating and lethal lung disease with no current cure. Research into cellular signaling pathways able to modulate aspects of pulmonary inflammation and fibrosis will aid in the development of effective therapies for its treatment. Our laboratory has generated a transgenic/knockout mouse with systemic elevations in adenosine due to the partial lack of its metabolic enzyme, adenosine deaminase (ADA). These mice spontaneously develop progressive lung inflammation and severe pulmonary fibrosis suggesting that aberrant adenosine signaling is influencing the development and/or progression of the disease in these animals. These mice also show marked increases in the pro-fibrotic mediator, osteopontin (OPN), which are reversed through ADA therapy that serves to lower lung adenosine levels and ameliorate aspects of the disease. OPN is known to be regulated by intracellular signaling pathways that can be accessed through adenosine receptors, particularly the low affinity A2BR receptor, suggesting that adenosine receptor signaling may be responsible for the induction of OPN in our model. In-vitro, adenosine and the broad spectrum adenosine receptor agonist, NECA, were able to induce a 2.5-fold increase in OPN transcripts in primary alveolar macrophages. This induction was blocked through antagonism of the A2BR receptor pharmacologically, and through the deletion of the receptor subtype in these cells genetically, supporting the hypothesis that the A2BR receptor was responsible for the induction of OPN in our model. These findings demonstrate for the first time that adenosine signaling is an important modulator of pulmonary fibrosis in ADA-deficient mice and that this is in part due to signaling through the A2BR receptor which leads to the induction of the pro-fibrotic molecule, otseopontin. ^

The two -state model of receptor activation: The agonist and the efficacy

The Two State model describes how drugs activate receptors by inducing or supporting a conformational change in the receptor from “off” to “on”. The beta 2 adrenergic receptor system is the model system which was used to formalize the concept of two states, and the mechanism of hormone agonist stimulation of this receptor is similar to ligand activation of other seven transmembrane receptors. Hormone binding to beta 2 adrenergic receptors stimulates the intracellular production of cyclic adenosine monophosphate (cAMP), which is mediated through the stimulatory guanyl nucleotide binding protein (Gs) interacting with the membrane bound enzyme adenylylcyclase (AC). ^ The effects of cAMP include protein phosphorylation, metabolic regulation and transcriptional regulation. The beta 2 adrenergic receptor system is the most well known of its family of G protein coupled receptors. Ligands have been scrutinized extensively in search of more effective therapeutic agents at this receptor as well as for insight into the biochemical mechanism of receptor activation. Hormone binding to receptor is thought to induce a conformational change in the receptor that increases its affinity for inactive Gs, catalyzes the release of GDP and subsequent binding of GTP and activation of Gs. ^ However, some beta 2 ligands are more efficient at this transformation than others, and the underlying mechanism for this drug specificity is not fully understood. The central problem in pharmacology is the characterization of drugs in their effect on physiological systems, and consequently, the search for a rational scale of drug effectiveness has been the effort of many investigators, which continues to the present time as models are proposed, tested and modified. ^ The major results of this thesis show that for many b2 -adrenergic ligands, the Two State model is quite adequate to explain their activity, but dobutamine (+/−3,4-dihydroxy-N-[3-(4-hydroxyphenyl)-1-methylpropyl]- b -phenethylamine) fails to conform to the predictions of the Two State model. It is a weak partial agonist, but it forms a large amount of high affinity complexes, and these complexes are formed at low concentrations much better than at higher concentrations. Finally, dobutamine causes the beta 2 adrenergic receptor to form high affinity complexes at a much faster rate than can be accounted for by its low efficiency activating AC. Because the Two State model fails to predict the activity of dobutamine in three different ways, it has been disproven in its strictest form. ^