Construction of a novel cell-based assay for the evaluation of anti-EGFR drug efficacy against EGFR mutation.

Epidermal growth factor receptor (EGFR) overexpression and EGFR-mediated signaling pathway dysregulation have been observed in tumors from patients with various cancers, especially non-small cell lung cancer. Thus, several anti-EGFR drugs have been developed for cancer therapy. For patients with known EGFR activating mutations (EGFR exon 19 in-frame deletions and exon 21 L858R substitution), treatment with an EGFR tyrosine kinase inhibitor (EGFR TKI; gefitinib, erlotinib or afatinib) represents standard first-line therapy. However, the clinical efficacy of these TKIs is ultimately limited by the development of acquired drug resistance such as by mutation of the gatekeeper T790 residue (T790M). To overcome this acquired drug resistance and develop novel anti-EGFR drugs, a cell-based assay system for EGFR TKI resistance mutant-selective inhibitors is required. We constructed a novel cell-based assay for the evaluation of EGFR TKI efficacy against EGFR mutation. To this end, we established non-tumorigenic immortalized breast epithelial cells that proliferate dependent on EGF (MCF 10A cells), which stably overexpress mutant EGFR. We found that the cells expressing EGFR containing the T790M mutation showed higher resistance against gefitinib, erlotinib and afatinib compared with cells expressing wild-type EGFR. In contrast, L858R mutant-expressing cells exhibited higher TKI sensitivity. The effect of T790M-selective inhibitors (osimertinib and rociletinib) on T790M mutant-expressing cells was significantly higher than gefitinib and erlotinib. Finally, when compared with commercially available isogenic MCF 10A cell lines carrying introduced mutations in EGFR, our EGFR mutant-overexpressing cells exhibited obviously higher responsiveness to EGFR TKIs depending on the underlying mutations because of the higher levels of EGFR phosphorylation in the EGFR mutant-overexpressing cells than in the isogenic cell lines. In conclusion, we successfully developed a novel cell-based assay for evaluating the efficacy of anti-EGFR drugs against EGFR mutation.

Differential responses of normal human coronary artery endothelial cells against multiple cytokines comparatively assessed by gene expression profiles.

Endothelial cells play an important role in terms of biological functions by responding to a variety of stimuli in the blood. However, little is known about the molecular mechanism involved in rendering the variety in the cellular response. To investigate the variety of the cellular responses against exogenous stimuli at the gene expression level, we attempted to describe the cellular responses with comprehensive gene expression profiles, dissect them into multiple response patterns, and characterize the response patterns according to the information accumulated so far on the genes included in the patterns. We comparatively analyzed in parallel the gene expression profiles obtained with DNA microarrays from normal human coronary artery endothelial cells (HCAECs) stimulated with multiple cytokines, interleukin-1beta, tumor necrosis factor-alpha, interferon-beta, interferon-gamma, and oncostatin M, which are profoundly involved in various functional responses of endothelial cells. These analyses revealed that the cellular responses of HCAECs against these cytokines included at least 15 response patterns specific to a single cytokine or common to multiple cytokines. Moreover, we statistically extracted genes contained within the individual response patterns and characterized the response patterns with the genes referring to the previously accumulated findings including the biological process defined by the Gene Ontology Consortium (GO). Out of the 15 response patterns in which at least one gene was successfully extracted through the statistical approach, 11 response patterns were differentially characterized by representing the number of genes contained in individual criteria of the biological process in the GO only. The approach to dissect cellular responses into response patterns and to characterize the pattern at the gene expression level may contribute to the gaining of insight for untangling the diversity of cellular functions.