Antitumor activity of histone deacetylase inhibitors in non-small cell lung cancer cells: development of a molecular predictive model.

To ascertain the potential for histone deacetylase (HDAC) inhibitor-based treatment in non-small cell lung cancer (NSCLC), we analyzed the antitumor effects of trichostatin A (TSA) and suberoylanilide hydroxamic acid (vorinostat) in a panel of 16 NSCLC cell lines via 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay. TSA and vorinostat both displayed strong antitumor activities in 50% of NSCLC cell lines, suggesting the need for the use of predictive markers to select patients receiving this treatment. There was a strong correlation between the responsiveness to TSA and vorinostat (P < 0.0001). To identify a molecular model of sensitivity to HDAC inhibitor treatment in NSCLC, we conducted a gene expression profiling study using cDNA arrays on the same set of cell lines and related the cytotoxic activity of TSA to corresponding gene expression pattern using a modified National Cancer Institute program. In addition, pathway analysis was done with Pathway Architect software. We used nine genes, which were identified by gene-drug sensitivity correlation and pathway analysis, to build a support vector machine algorithm model by which sensitive cell lines were distinguished from resistant cell lines. The prediction performance of the support vector machine model was validated by an additional nine cell lines, resulting in a prediction value of 100% with respect to determining response to TSA and vorinostat. Our results suggested that (a) HDAC inhibitors may be promising anticancer drugs to NSCLC and (b) the nine-gene classifier is useful in predicting drug sensitivity to HDAC inhibitors and may contribute to achieving individualized therapy for NSCLC patients.

Anticancer drug clustering in lung cancer based on gene expression profiles and sensitivity database.

BACKGROUND: The effect of current therapies in improving the survival of lung cancer patients remains far from satisfactory. It is consequently desirable to find more appropriate therapeutic opportunities based on informed insights. A molecular pharmacological analysis was undertaken to design an improved chemotherapeutic strategy for advanced lung cancer. METHODS: We related the cytotoxic activity of each of commonly used anti-cancer agents (docetaxel, paclitaxel, gemcitabine, vinorelbine, 5-FU, SN38, cisplatin (CDDP), and carboplatin (CBDCA)) to corresponding expression pattern in each of the cell lines using a modified NCI program. RESULTS: We performed gene expression analysis in lung cancer cell lines using cDNA filter and high-density oligonucleotide arrays. We also examined the sensitivity of these cell lines to these drugs via MTT assay. To obtain our reproducible gene-drug sensitivity correlation data, we separately analyzed two sets of lung cancer cell lines, namely 10 and 19. In our gene-drug correlation analyses, gemcitabine consistently belonged to an isolated cluster in a reproducible fashion. On the other hand, docetaxel, paclitaxel, 5-FU, SN-38, CBDCA and CDDP were gathered together into one large cluster. CONCLUSION: These results suggest that chemotherapy regimens including gemcitabine should be evaluated in second-line chemotherapy in cases where the first-line chemotherapy did not include this drug. Gene expression-drug sensitivity correlations, as provided by the NCI program, may yield improved therapeutic options for treatment of specific tumor types.

Significance of osteopontin in the sensitivity of malignant pleural mesothelioma to pemetrexed.

Pemetrexed (PEM) is currently recommended as one of the standard anticancer drugs for malignant pleural mesothelioma (MPM). However, the mechanism of the sensitivity of MPM to PEM remains unclear. We analyzed the antitumor effects of PEM in six MPM cell lines by MTS assay. To identify genes associated with drug sensitivity, we conducted gene expression profiling on the same set of cell lines using GeneChips and pathway analysis. Three cell lines were sensitive to PEM. A total fo 18 transcripts and 14 genes identified by GeneChips were significantly correlated with sensitivity to PEM. Pathway analysis revealed that osteopontin (SPP1/OPN) was an important target in PEM sensitivity. Overexpression of SPP1/OPN was observed in the sensitive cells by quantitative PCR and western blot analysis. Introduction of SPP1/OPN by lentiviral vector significantly enhanced the invasion activities of MPM cells. PEM treatment with SPP1/OPN knockdown inhibited the PEM-induced cell growth-inhibitory effect in PEM-sensitive cells. Expression of SPP1/OPN and AKT phosphorylation significantly decreased after PEM treatment of the PEM-sensitive cells. High immunohistochemical expression of SPP1/OPN was observed in two of three MPM patients who had a partial response to PEM-based chemotherapy. PEM has antitumor effects in MPM cells dependent on SPP1/OPN overexpression resulting in AKT activation. Our results suggest that SPP1 may be used as a single predictive biomarker of the effectiveness of PEM treatment in MPM.

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

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.