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Previously, we performed a molecular pharmacological study that applied a combination of DNA microarray-based gene expression profiling and drug sensitivity tests in vitro with a view to designing an improved chemotherapeutic strategy for advanced lung cancer. Utilizing recent key technological advances in proteomics, particularly antibody array-based methodologies, the current study aimed to examine the benefit of protein expression profiling in an analogous molecular pharmacological context. We performed protein expression analysis in a panel of lung cancer cell lines via an antibody array approach. Using a modified NCI program, we related cell line-specific proteomic profiles to the previously determined cytotoxic activity of a selection of commonly used anticancer agents, namely docetaxel, paclitaxel, gemcitabine, vinorelbine, 5-fluorouracil (5-FU), SN38, cisplatin (CDDP) and carboplatin (CBDCA). In addition, we compared these results with those obtained from our prior DNA microarray-based transcriptomic study. In our expression-drug correlation analysis using antibody array, gemcitabine consistently belonged to an isolated cluster. Docetaxel, paclitaxel, 5-FU, SN38, CBDCA and CDDP were gathered together into one large cluster. These results coincided with those generated by the prior transcriptomic study. Various genes were commonly listed that differentiated gemcitabine from the others. The identified factors associated with drug sensitivities were different between both analyses. Our proteomic profiling data provided confirmation of the previous transcript expression-drug sensitivity correlation analysis. These results suggest that chemotherapy regimens that include gemcitabine should be evaluated in second-line chemotherapy in cases where the first-line chemotherapy did not include this drug. Protein expression-drug sensitivity correlations in lung cancer cells in vitro may provide useful information in determining the most appropriate therapeutic options for lung cancer patients.

Anticancer drug clustering based on proteomic profiles and a sensitivity database in a lung cancer cell line panel