In vitro simulation study of individualized chemotherapy in lung cancer.

The primary aim of this in vitro simulation study was to evaluate the utility of gene expression profile analysis in predicting the effect of varying drug combinations for the treatment of lung cancer. Using 10 human cancer cell lines, we focused our gene expression analysis on a cohort of candidate sensitivity-prediction factors, previously reported using cDNA filter arrays, with a view to predicting the ability of a set of anti-cancer drugs commonly used to treat lung cancer, namely cisplatin, 5-fluorouracil (5FU), SN38, docetaxel, gemcitabine, and vinorelbine. Altered expression of genes for glutathione-S-transferase-pi, uridine phosphorylase, O-6-methylguanine-DNA methyltransferase, and multidrug resistance 1 was identified in lung cancer cell lines. Drug sensitivity testing, in the form of methylthiotetrazol analysis, was performed using these six anti-cancer drugs against the panel of 10 lung cancer cell lines. We compared the predicted chemosensitivity based on the gene expression pattern of 19 well-known sensitivity-related genes with the cytotoxic activity of each of these anti-cancer drugs. Molecular profiling data predicted resistance to CDDP in LK-2 cells, 5FU in LK-2, PC7, A549, NCI-N231, Lu135 cells, irinitecan in PC9 cells, and VNR in PC7 cells. However, the prediction efficacy (number of predicted inactive drugs by gene expression analysis/number of inactive drugs by methylthiotetrazol assay) was 21.6% (8 of 37). No false-positive findings in relation to sensitivity-related genes were obtained on the basis of this molecular analysis. Thus, prediction of sensitivity to lung cancer by molecular analysis appears possible. With elucidation of additional drug sensitivity factors, selection of appropriate anticancer drugs by gene expression profiling may make it possible to increase the response rate in lung cancer chemotherapy.