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

Abstract

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.

Figure 1

Clustered image map (CIM) relating drug activity to gene expression in lung cancer cell lines – Experiment 1. (A) using filter array, (B) using GeneChip. The cluster trees of drugs (y axis) and gene expression (x axis) were shown in the CIM. Each block of colors represents correlations between clusters of genes and drugs; red (high positive correlation) and blue (negative correlation). In this analysis, gemcitabine belonged to an isolated cluster (Fig. 1A,B). GEM; gemcitabine, PAC; paclitaxel, DOC; docetaxel, VIN; vinorelbine

Figure 2

Clustered image map (CIM) relating drug activity to gene expression in NSCLC cell lines – Experiment 1. (A) using filter array, (B) using GeneChip. The cluster trees of drugs (y axis) and gene expression (x axis) were shown in the CIM. Each block of colors represents correlations between clusters of genes and drugs; red (high positive correlation) and blue (negative correlation). In this gemcitabine belonged to an isolated cluster (Fig. 2A,B). GEM; gemcitabine, PAC; paclitaxel, DOC; docetaxel, VIN; vinorelbine

Figure 3

Clustered image map (CIM) relating drug activity to gene expression in lung cancer cell lines using GeneChip – Experiment 2. (A) all lung cancer cell lines, (B) NSCLC. The cluster trees of drugs (y axis) and gene expression (x axis) were shown in the CIM. Each block of colors represents correlations between clusters of genes and drugs; red (high positive correlation) and blue (negative correlation). In this analysis, gemcitabine belonged to an isolated cluster (Fig. 3A,B). GEM; gemcitabine, PAC; paclitaxel, DOC; docetaxel, VIN; vinorelbine