Statistics and Probability Commons^™

Gene Expression Patterns That Predict Sensitivity To Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors In Lung Cancer Cell Lines And Human Lung Tumors, Justin M. Balko, Anil Potti, Christopher Saunders, Arnold J. Stromberg, Eric B. Haura, Esther P. Black

Statistics Faculty Publications

BACKGROUND: Increased focus surrounds identifying patients with advanced non-small cell lung cancer (NSCLC) who will benefit from treatment with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKI). EGFR mutation, gene copy number, coexpression of ErbB proteins and ligands, and epithelial to mesenchymal transition markers all correlate with EGFR TKI sensitivity, and while prediction of sensitivity using any one of the markers does identify responders, individual markers do not encompass all potential responders due to high levels of inter-patient and inter-tumor variability. We hypothesized that a multivariate predictor of EGFR TKI sensitivity based on gene expression data would offer a …

Identification Of Gene Expression Patterns Using Planned Linear Contrasts, Hao Li, Constance L. Wood, Yushu Liu, Thomas V. Getchell, Marilyn L. Getchell, Arnold J. Stromberg

Statistics Faculty Publications

BACKGROUND: In gene networks, the timing of significant changes in the expression level of each gene may be the most critical information in time course expression profiles. With the same timing of the initial change, genes which share similar patterns of expression for any number of sampling intervals from the beginning should be considered co-expressed at certain level(s) in the gene networks. In addition, multiple testing problems are complicated in experiments with multi-level treatments when thousands of genes are involved.

RESULTS: To address these issues, we first performed an ANOVA F test to identify significantly regulated genes. The Benjamini and …