Bepler, Gerold

Abstract Cigarette smoking is a major risk factor in the development of non–small cell lung cancer (NSCLC), which accounts for 80% of all lung cancers. Nicotine, the major addictive component of tobacco smoke, can induce proliferation, invasion, and epithelial-to-mesenchymal transition (EMT) in NSCLC cell lines and promote metastasis of NSCLC in mice. Here, we demonstrate that the scaffolding protein β-arrestin-1 is necessary for nicotine-mediated induction of mesenchymal genes vimentin and fibronectin as well as EMT regulators ZEB1 and ZEB2. Nicotine induced changes in cell morphology and ablate tight junctions consistent with EMT; β-arrestin-1, but not β-arrestin-2, was required for these changes. β-Arrestin-1 promoted the expression of the mesenchymal genes, as well as ZEB1 and ZEB2, through the mediation of the E2F1 transcription factor; this required Src kinase activity. Stimulation of multiple NSCLC cell lines with nicotine led to enhanced recruitment of β-arrestin-1 and E2F1 on vimentin, fibronectin, and ZEB1 and ZEB2 promoters. Furthermore, there was significantly more β-arrestin-1 and E2F1 associated with these promoters in human NSCLC tumors, and β-arrestin-1 levels correlated with vimentin and fibronectin levels in human NSCLC samples. A549-luciferase cells lacking β-arrestin-1 showed a significantly reduced capacity for tumor growth and metastasis when orthotopically implanted into the lungs of SCID-beige mice. Taken together, these studies reveal a novel role for β-arrestin-1 in the growth and metastasis of NSCLC. Cancer Res; 75(6); 1009–20. ©2015 AACR.

Providing personalized treatments designed to maximize benefits and minimizing harms is of tremendous current medical interest. One problem in this area is the evaluation of the interaction between the treatment and other predictor variables. Treatment effects in subgroups having the same direction but different magnitudes are called quantitative interactions, whereas those having opposite directions in subgroups are called qualitative interactions (QIs). Identifying QIs is challenging because they are rare and usually unknown among many potential biomarkers. Meanwhile, subgroup analysis reduces the power of hypothesis testing and multiple subgroup analyses inflate the type I error rate. We propose a new Bayesian approach to search for QI in a multiple regression setting with adaptive decision rules. We consider various regression models for the outcome. We illustrate this method in two examples of phase III clinical trials. The algorithm is straightforward and easy to implement using existing software packages. We provide a sample code in Appendix A. Copyright © 2012 John Wiley & Sons, Ltd.

On the basis of this data set of participating institutions in Florida, several areas in the care of patients with non–small-cell lung cancer were identified as targets for future quality improvement efforts.