Gene expression patterns that predict sensitivity to epidermal growth factor receptor tyrosine kinase inhibitors in lung cancer cell lines and human lung tumors.

摘要: Increased focus surrounds identifying patients with advanced non-small cell lung cancer (NSCLC) who will benefit from treatment epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKI). EGFR mutation, gene copy number, coexpression of ErbB proteins and ligands, epithelial to mesenchymal transition markers all correlate TKI sensitivity, while prediction sensitivity using any one the does identify responders, individual do not encompass potential responders due high levels inter-patient inter-tumor variability. We hypothesized that a multivariate predictor based on expression data would offer clinically useful method accounting for increased variability inherent in predicting response elucidation mechanisms aberrant signalling. Furthermore, we anticipated this methodology result improved predictions compared single parameters alone both vitro vivo. Gene derived lines demonstrate differential TKI, such as erlotinib, were used generate models priori response. The signature displays significant biological relevance biology pertinent signalling molecules downstream effector are present signature. Diagonal linear discriminant analysis was highly effective classifying out-of-sample by inhibition, more accurate than mutational status alone. Using same predictor, classified human adenocarcinomas captured majority tumors activation well those harbouring activating mutations domain. have demonstrated predictive can classify adenocarcinomas. These suggest predictors clinical use likely provide robust is achieved biomarkers or characteristics cancers.