We have established a panel of 45 human cancer cell lines (JFCR-45) to explore genes that determine the chemosensitivity of these cell lines to anticancer drugs. JFCR-45 comprises cancer cell lines derived from tumors of three different organs: breast, liver, and stomach. The inclusion of cell lines derived from gastric and hepatic cancers is a major point of novelty of this study. We determined the concentration of 53 anticancer drugs that could induce 50% growth inhibition (GI50) in each cell line. Cluster analysis using the GI50s indicated that JFCR-45 could allow classification of the drugs based on their modes of action, which coincides with previous findings in NCI-60 and JFCR-39. We next investigated gene expression in JFCR-45 and developed an integrated database of chemosensitivity and gene expression in this panel of cell lines. We applied a correlation analysis between gene expression profiles and chemosensitivity profiles, which revealed many candidate genes related to the sensitivity of cancer cells to anticancer drugs. To identify genes that directly determine chemosensitivity, we further tested the ability of these candidate genes to alter sensitivity to anticancer drugs after individually overexpressing each gene in human fibrosarcoma HT1080. We observed that transfection of HT1080 cells with the HSPA1A and JUN genes actually enhanced the sensitivity to mitomycin C, suggesting the direct participation of these genes in mitomycin C sensitivity. These results suggest that an integrated bioinformatical approach using chemosensitivity and gene expression profiling is useful for the identification of genes determining chemosensitivity of cancer cells.

Predicting the chemosensitivity of individual patients is important to improve the efficacy of cancer chemotherapy. An approach to this end is to understand the genes that determine the chemosensitivity of cancer cells. Many genes have been described that determine the sensitivity to multiple drugs, including drug transporters (1–3) and metabolizing enzymes (4–6). Genes determining the sensitivity to specific drugs have also been reported. For example, increased activities of γ-glutamyl hydrolase (7) and dihydrofolate reductase (8) are resistant factors for methotrexate; increased activities of thymidylate synthase (9), metallothionein (10), and cytidine deaminase (11) are resistant factors for 5-fluorouracil (5-FU), cisplatin, 1-β-d-arabinofuranosylcytosine, respectively; and increased activity of NQO1 (12) is a sensitive factor for mitomycin C (MMC). However, the chemosensitivity of cancer cells is not determined by a handful of genes. These genes are not sufficient to explain the variation of the chemosensitivity of cancer cells.

Recently, attempts were made to predict the chemosensitivity of cancers using genome-wide expression profile analyses, such as cDNA microarray and single nucleotide polymorphisms (13–18). For example, Scherf et al. (18) and Zembutsu et al. (15) reported the analysis of genes associated with sensitivity to anticancer drugs in a panel of human cancer cell lines and in human cancer xenografts, respectively. Tanaka et al. (17) presented prediction models of anticancer efficacy of eight drugs using real-time PCR expression analysis of 12 genes in cancer cell lines and clinical samples. We also analyzed chemosensitivity-related genes in 39 human cancer cell lines (JFCR-39; ref. 19) and validated the association of some of these genes to chemosensitivity using additional cancer cell lines (20). These genes can be used as markers to predict chemosensitivity. Moreover, some of these genes may directly determine the chemosensitivity of cancer cells.

In the present study, we established a new panel of 45 human cancer cell lines (JFCR-45) derived from tumors from three different organs: breast, liver, and stomach. Using JFCR-45, we attempted to analyze the heterogeneity of chemosensitivity in breast, liver, and stomach cancers. We assessed their sensitivity to 53 anticancer drugs and developed a database of chemosensitivity. Then, we analyzed gene expression in 42 human cancer cell lines using cDNA arrays and stored them in the gene expression database. Using these two databases, we extracted genes whose expression was correlated to chemosensitivity. We further screened them to identify genes that could change the sensitivity to anticancer drugs using an in vitro gene transfection assay.

Cell Lines and Cell Cultures

We established a panel of JFCR-45 that included a portion of JFCR-39 and the 12 stomach cancer cell lines described previously (19, 20). They consist of the following cell lines: breast cancer cells HBC-4, BSY1, HBC-5, MCF-7, MDA-MB-231, KPL-3C (21), KPL-4, KPL-1, T-47D (22), HBC-9, ZR-75-1 (23), and HBC-8; liver cancer cells HepG2, Hep3B, Li-7, PLC/PRF/5, HuH7, HLE, HLF (24), HuH6 (25), RBE, SSP-25 (26), HuL-1 (27), and JHH-1 (28); and stomach cancer cells St-4, MKN1, MKN7, MKN28, MKN45, MKN74, GCIY, GT3TKB, HGC27, AZ521 (29), 4-1ST, NUGC-3, NUGC-3/5-FU, HSC-42, AGS, KWS-1, TGS-11, OKIBA, ISt-1, ALF, and AOTO. The AZ521 cell line was obtained from the Cell Resource Center for Biomedical Research, Institute of Development, Aging and Cancer, Tohoku University (Sendai, Japan). The 4-1ST, OKIBA, and AOTO cell lines were provided by Dr. Tokuji Kawaguchi (Department of Pathology, Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan). All cell lines were cultured in RPMI 1640 (Nissui Pharmaceutical, Tokyo, Japan) with 5% fetal bovine serum, penicillin (100 units/mL), and streptomycin (100 μg/mL) at 37°C under 5% CO2.

Determination of the Sensitivity to Anticancer Drugs

Growth inhibition experiments were done to assess the chemosensitivity to anticancer drugs. Growth inhibition was measured by determining the changes in the amounts of total cellular protein after 48 hours of drug treatment using a sulforhodamine B assay. The GI50 values, which represent 50% growth inhibition concentration, were evaluated as described before (30, 31). Several experiments were done to determine the median GI50 value for each drug. Absolute values were then log transformed for further analysis.

Anticancer Drugs and Compounds

Actinomycin D, 5-FU, tamoxifen, cytarabine, radicicol, melphalan, 6-mercaptopurine, 6-thioguanine, and colchicine were purchased from Sigma (St. Louis, MO). The anticancer agents in clinical use were obtained from the company specified in parentheses, and those under development were kindly provided by the company specified as described below: aclarubicin and neocarzinostatin (Yamanouchi Pharmaceutical, Tokyo, Japan); oxaliplatin (Asahi Kasei, Tokyo, Japan), HCFU (Nihon Schering, Osaka, Japan); doxifluridine (Chugai Pharmaceutical, Tokyo, Japan); toremifene, bleomycin, and estramustine (Nippon Kayaku, Tokyo, Japan); daunorubicin and pirarubicin (Meiji, Tokyo, Japan); doxorubicin, epirubicin, MMC, vinorelbine, and l-asparaginase (Kyowa Hakko Kogyo, Tokyo, Japan); peplomycin, etoposide, NK109, and NK611 (Nippon Kayaku); vinblastine, vincrinstine, IFN-γ, and 4-hydroperoxycyclophosphamide (Shionogi, Tokyo, Japan); carboplatin and cisplatin (Bristol-Myers Squibb, New York, NY); mitoxantrone and methotrexate (Wyeth Lederie Japan, Tokyo, Japan); cladribine (Janssen Pharmaceutical, Titusville, NJ); amsacrine (Pfizer Pharmaceutical, formerly Warner Lambert, Plymouth, MI); camptothecin, irinotecan, and SN-38 (Yakult, Tokyo, Japan); paclitaxel (Bristol-Myers Squibb); docetaxel and topotecan (Aventis Pharma, Strasbourg, France); IFN-α (Sumitomo Pharmaceutical, Osaka, Japan); IFN-β (Daiichi Pharmaceutics, Tokyo, Japan); gemcitabine (Eli Lilly Japan, Kobe, Japan); E7010 and E7070 (Eisai, Tokyo, Japan); dolastatine 10 (Teikoku Hormone MFG, Tokyo, Japan); and TAS103 (Taiho Pharmaceutical Co., Tokyo, Japan).

Gene Expression Profiles by cDNA Array

Expression profiles of 3,537 genes in 42 human cancer cell lines were examined using Atlas Human 3.6 Array (BD Biosciences Clontech, Inc., Franklin Lakes, NJ) in duplicates. Experiments were done according to the manufacturer's instructions. Briefly, cell lines were harvested in log phase. Total RNA was extracted with TRIzol reagent (Invitrogen, Inc., Carlsbad, CA) and purified with Atlas Pure Total RNA Labeling System. Purified total RNAs were converted to 32P-labeled cDNA probe by SuperScript II (Invitrogen). cDNA probe was hybridized to the Atlas Array overnight at 68°C and washed. Hybridized array was detected with PhosphorImager (Molecular Dynamics, Inc. Sunnyvale, CA). Scanned data were transformed to the numerical value with Atlas Image 2.0 software (BD Biosciences Clontech) and normalized by dividing by the value of 90% percentile of all genes in each experiment. Then, the intensities of the genes were defined by the average of intensities of duplicate results. The genes whose expression levels differed more than twice between the duplicates were eliminated from subsequent analysis. When the intensities of gene expression in both arrays were below the threshold value, they were given the value of threshold and were used for analysis. We determined the values of threshold of the normalized data as 30% of the value of 90% percentile. Then, log2 was calculated for each expression value.

Hierarchical Clustering

Hierarchical clustering using average linkage method was done by “Gene Spring” software (Silicon Genetics, Inc. Redwood, CA). Pearson correlation coefficients were used to determine the degree of similarity.

Correlation Analysis between Gene Expression and Chemosensitivity Profiles

The genes whose expressions were observed in >50% of all cell lines examined were selected for the correlation analysis. The degree of similarity between chemosensitivity and gene expression were calculated using the following Pearson correlation coefficient formula:

\[\mathit{r}\ =\ \frac{{{\sum}_{\mathit{i}}}(\mathit{x_{i}}\ {-}\ \mathit{x_{m}})(\mathit{y_{i}}\ {-}\ \mathit{y_{m}})}{\sqrt{{{\sum}_{\mathit{i}}}(\mathit{x_{i}}\ {-}\ \mathit{x_{m}})^{2}{{\sum}_{\mathit{i}}}(\mathit{y_{i}}\ {-}\ \mathit{y_{m}})^{2}}}\]

where xi is the log expression data of the gene x in cell i, yi is the log sensitivity (∣log10GI50∣) of cell i to drug y, xm is the mean of the log expression data of the gene x, and ym is the mean sensitivity (∣log10GI50∣) of drug y. A significant correlation was defined as P < 0.05.

Screening of the Genes That Determine Chemosensitivity

Candidate genes related to the chemosensitivity were cloned into the vector pcDNA3.1/myc-His A (Invitrogen). Transfection of HT1080 cells with the plasmid DNA was carried out using LipofectAMINE Plus reagent (Invitrogen). The transfection efficiency was monitored by green fluorescent protein fluorescence. The fluorescence of green fluorescent protein was observed in >90% of the green fluorescent protein–transfected HT1080 (data not shown). Twenty-four hours after the transfection, proper concentrations of MMC were added and the cells were treated for 24 hours. Efficacies of anticancer drugs were determined by measuring the growth inhibition. Cell growth was measured by following [3H]thymidine incorporation. [3H]thymidine (0.067 MBq) was added to each well and incubated at 37°C for 45 minutes. Cells were washed with prewarmed PBS(−) and fixed with 10% TCA on ice for 2 hours. After fixing, cells were washed with 10% TCA and lysed with 0.1% SDS-0.2 N NaOH solution. After incubation at 37°C, the lysed mixture was neutralized with 0.25 mol/L acetic acid solution. [3H]thymidine incorporated into the cells was determined using scintillation counter. All experiments, except for interleukin (IL)-18, were done four times.

Sensitivity of JFCR-45 to 53 Anticancer Drugs

Sensitivity to 53 drugs was assessed as described in Materials and Methods. The known modes of actions and the value of ∣log10GI50∣ of 53 anticancer drugs in each of the 45 cell lines are summarized in Table 1. The ∣log10GI50∣ indicated here is the median value of multiple experiments. The chemosensitivity of the cell lines differed even among those derived from the same organ. These data were stored in a chemosensitivity database. Figure 1 shows the classification of the anticancer drugs by hierarchical clustering analysis based on chemosensitivity, ∣log10GI50∣, of JFCR-45. As shown, the 53 drugs were classified into several clusters, each consisting of drugs with similar modes of action [e.g., one cluster included topoisomerase (topo) I inhibitors, such as camptothecin, topotecan, and SN-38]. The second cluster comprised tubulin binders, including taxanes and Vinca alkaloids. 5-FU and its derivatives were also clustered into a single group. These results indicated that our system using JFCR-45 was able to classify the drugs based on their modes of action, which is in agreement with previous findings using NCI-60 and JFCR-39 (18, 19, 32).

Table 1.

The mode of actions and the median value of ∣log10GI50∣ of 53 anticancer drugs in each of the 45 cell lines

Drug nameTarget/mode of actionBreast
Liver
Stomach
HBC-4BSY1HBC-5MCF-7MDA-MB-231KPL-3CKPL-4KPL-1T-47DHBC-9ZR-75-1HBC-8HepG2Hep3BLi-7PLC/PRF/5HuH7HLEHLFHuH6RBESSP-25HuL-1JHH-1St-4MKN1MKN7MKN28MKN45MKN74GCIYGT3 TKBHGC27AZ5214-1STNUGC -3NUGC -3/5-FUHSC-42AGSKWS-1TGS-11OKIBAISt-1ALFAOTO
Aclarubicin DNA/RNA synthesis 7.04 8.69 7.92 7.86 7.83 7.11 7.63 7.95 7.39 7.08 8.03 7.93 8.13 7.77 7.39 7.68 8.29 7.49 7.86 7.70 7.87 7.39 7.97 8.23 7.88 8.09 7.73 7.25 8.59 7.43 8.00 7.86 7.13 8.49 7.96 9.04 7.51 8.21 8.27 7.96 8.31 7.20 7.19 8.54 7.57 
Oxaliplatin DNA cross-linker 5.79 5.75 5.40 5.69 4.75 5.04 5.20 4.78 5.17 4.10 5.08 6.17 7.07 5.39 5.78 5.61 6.44 4.90 4.75 5.60 5.19 4.58 6.04 6.01 4.75 5.04 4.42 4.58 6.84 4.93 5.71 5.31 5.10 6.16 5.17 6.18 5.23 5.98 5.58 6.26 7.02 5.85 5.14 5.46 4.78 
Actinomycin D RNA synthesis 9.20 9.10 8.85 9.45 8.71 8.90 9.05 9.04 8.89 8.24 8.98 9.60 9.03 8.61 8.24 8.04 8.99 8.13 8.45 8.75 8.25 8.47 8.78 9.00 7.99 8.74 8.77 9.02 9.39 9.20 8.24 9.12 8.76 9.55 8.80 8.85 8.56 9.32 8.99 9.22 9.55 9.35 8.77 9.39 8.88 
HCFU Pyrimidine 4.36 5.17 4.44 5.13 4.57 4.65 5.55 4.41 4.97 4.22 4.68 4.84 5.28 4.80 4.79 4.56 4.99 4.67 4.70 4.50 4.92 4.69 4.87 4.63 4.17 4.70 4.82 4.77 5.56 4.86 4.77 5.09 4.74 5.21 4.84 4.74 4.36 4.89 5.00 4.71 4.27 5.10 4.15 4.23 4.44 
5-FU Pyrimidine 4.43 4.87 4.40 5.12 4.18 4.00 5.23 4.00 4.13 4.00 4.70 5.11 5.27 4.20 4.26 4.21 5.08 4.00 4.19 4.00 4.60 4.00 5.29 4.72 4.35 4.40 4.26 4.27 5.46 4.22 4.60 5.09 4.34 5.12 4.04 4.67 4.00 4.40 5.02 4.50 4.06 6.38 4.00 4.42 4.09 
Doxifluridine Pyrimidine 4.00 4.42 4.00 4.00 4.00 4.00 4.09 4.00 4.00 4.00 4.14 4.19 4.49 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.04 4.00 4.00 4.00 4.01 4.00 4.20 4.00 4.00 4.00 4.00 4.00 4.00 4.02 4.00 4.00 4.26 4.00 4.00 4.18 4.00 4.00 4.00 
E7070 Cell cycle inhibitor 4.50 6.20 4.22 4.50 4.35 4.94 5.01 4.74 4.69 4.00 4.38 4.98 5.47 4.99 4.77 4.44 5.36 4.61 4.43 4.74 5.09 4.29 4.29 4.87 4.43 6.03 4.90 5.48 4.55 5.20 5.04 4.82 5.69 6.02 4.88 5.75 4.39 4.81 4.46 5.25 4.96 6.05 4.83 6.69 4.97 
Tamoxifen Estrogen receptor 4.95 5.42 5.01 5.04 4.90 5.14 5.49 4.93 5.31 4.90 4.95 5.53 5.45 5.30 5.23 4.79 5.09 5.02 4.97 5.38 4.90 5.11 4.87 4.97 4.95 4.89 5.44 5.23 5.13 5.67 4.92 5.19 5.25 5.11 4.87 5.06 4.86 4.89 5.59 4.93 5.20 5.58 4.93 5.43 5.13 
Toremifene Estrogen receptor 4.81 5.12 4.87 4.96 4.85 4.93 5.13 4.88 5.17 4.89 4.88 4.86 5.06 4.97 4.92 4.82 4.99 5.09 4.91 4.95 4.92 5.00 4.80 5.10 4.81 4.92 4.90 4.82 4.93 5.23 4.85 4.92 5.07 5.09 4.87 4.96 4.85 4.88 5.00 4.93 5.07 5.58 4.88 5.50 5.24 
MS-247 DNA synthesis 6.08 6.79 5.32 6.78 5.98 6.09 6.16 5.86 6.63 6.42 6.88 6.71 6.33 5.84 6.35 5.23 6.02 6.58 6.42 5.82 5.66 6.37 5.67 6.82 5.66 5.72 6.27 5.59 7.32 6.62 5.71 6.88 6.76 7.58 7.09 6.62 5.64 7.11 7.01 6.74 6.67 6.20 5.70 5.70 5.63 
Daunorubicin DNA synthesis/topo II 6.96 7.34 6.82 7.68 6.83 6.77 7.25 6.84 7.41 6.92 7.39 7.97 7.48 7.10 6.83 6.39 7.29 7.55 7.49 6.98 7.18 6.73 7.08 7.51 6.60 7.30 6.98 7.03 7.66 6.88 6.79 7.55 7.17 7.98 7.18 7.74 6.85 7.57 7.42 6.99 6.93 7.59 6.37 6.94 6.80 
Doxorubicin DNA synthesis/topo II 7.13 7.26 6.85 7.58 6.66 6.74 7.38 6.76 7.36 6.94 7.12 7.85 7.29 6.77 6.88 5.83 7.04 7.39 7.25 6.87 6.89 6.68 6.89 7.31 6.39 7.45 6.79 6.71 7.32 6.70 6.39 7.14 6.86 7.87 6.68 7.66 6.47 7.33 7.53 6.91 6.90 8.00 6.01 6.34 6.54 
Epirubicin DNA synthesis/topo II 6.08 6.90 6.59 7.08 6.42 6.50 7.03 6.83 7.26 6.73 7.90 7.19 7.33 6.86 6.87 6.29 7.31 7.21 7.25 6.91 6.84 6.73 6.74 7.03 7.21 7.53 6.85 6.60 7.35 6.60 6.53 7.10 6.71 8.00 7.02 7.68 6.13 7.61 8.02 7.12 6.91 7.12 5.99 7.00 6.51 
Mitoxantrone DNA synthesis 6.28 7.12 6.00 8.06 6.50 6.40 6.83 6.38 7.11 6.96 8.02 7.44 7.95 6.51 7.88 6.51 6.76 7.60 7.67 6.71 7.37 7.59 6.11 7.15 6.82 7.52 6.57 6.52 7.79 6.68 6.87 7.82 6.83 8.79 7.38 7.59 6.18 7.70 7.75 7.21 6.74 8.56 5.76 6.14 6.37 
Pirarubicin DNA synthesis/topo II 8.97 9.00 8.34 9.00 8.47 8.62 9.00 8.39 9.00 8.22 9.00 9.00 9.00 8.58 9.00 8.26 9.00 9.00 9.00 8.59 8.98 9.00 8.95 9.00 8.31 8.97 8.55 8.57 9.00 8.53 8.81 9.00 8.56 9.00 8.86 9.00 8.65 9.00 9.00 8.99 8.58 8.81 8.16 8.68 8.57 
Topotecan Topo I 5.84 6.57 5.10 8.00 5.55 6.37 6.71 5.90 7.51 6.18 7.20 7.61 7.93 5.81 7.70 5.64 6.07 7.73 7.73 5.72 6.83 6.74 5.30 6.99 7.21 6.27 5.54 5.81 8.00 5.62 6.61 7.83 5.64 7.74 8.00 7.68 5.82 8.00 7.54 6.07 6.39 6.10 6.70 6.90 6.85 
SN-38 Topo I 7.98 7.52 5.56 8.56 6.12 6.75 7.40 6.60 8.25 6.13 7.92 7.75 8.43 6.37 8.21 6.03 6.75 8.28 8.31 5.91 7.05 7.47 5.69 7.74 6.83 6.63 6.16 6.16 8.71 6.17 6.89 8.49 6.04 8.49 8.78 8.28 6.31 8.61 8.70 6.81 6.66 7.07 7.29 7.46 7.28 
Camptothecin Topo I 5.92 6.57 6.04 7.63 5.86 6.67 6.60 6.70 7.12 5.80 7.21 6.92 7.44 6.19 7.48 5.86 6.35 7.42 7.53 6.10 6.69 6.79 6.16 6.92 7.13 6.39 5.82 5.50 7.99 5.62 6.81 7.53 5.49 7.61 7.75 7.73 6.00 7.76 7.23 6.36 6.64 6.81 6.43 6.72 6.96 
Bleomycin DNA synthesis 4.81 4.89 4.00 4.48 4.00 4.00 5.59 4.00 5.46 4.46 4.22 4.37 6.02 4.38 5.66 4.00 4.85 6.04 6.59 4.15 4.73 4.97 5.10 4.94 4.00 4.61 4.03 4.00 4.54 4.22 4.00 6.21 4.22 7.18 6.03 4.75 4.00 5.66 5.19 4.00 4.00 5.55 4.00 4.81 4.58 
Peplomycin DNA synthesis 4.90 5.84 4.00 5.22 4.27 4.61 6.29 4.08 5.37 4.52 4.72 5.25 6.73 4.72 6.40 4.45 5.46 5.86 6.56 4.01 5.12 5.83 5.35 5.34 4.00 4.80 4.56 4.09 5.18 4.82 4.39 5.96 4.68 7.32 6.16 4.92 4.05 6.00 5.82 4.65 4.08 5.92 4.23 5.04 4.78 
Neocarzinostatin DNA synthesis 7.35 8.00 6.03 8.17 6.55 6.42 7.61 6.18 7.26 7.06 7.26 8.10 8.22 6.72 7.81 6.34 6.92 7.60 7.80 6.57 7.27 7.53 6.67 7.09 6.17 6.92 6.58 6.47 8.38 7.19 6.95 7.74 6.92 8.58 7.59 8.00 6.54 7.89 7.78 6.84 6.60 7.05 6.54 6.74 7.24 
Irinotecan Topo I 4.86 5.09 4.00 5.46 4.28 4.30 4.91 4.11 5.21 4.15 4.47 5.24 5.18 4.36 5.61 4.00 4.33 5.25 5.13 4.11 4.37 4.64 4.05 4.78 4.00 4.41 4.29 4.02 5.41 4.26 4.44 5.24 4.00 5.58 5.39 5.41 4.06 5.48 5.50 4.25 4.58 4.64 4.42 4.56 4.71 
TAS103 Topo 6.81 7.22 6.37 7.66 6.57 6.45 7.20 6.17 7.25 6.16 7.13 7.60 7.56 6.57 7.68 6.64 6.95 7.81 7.87 6.55 7.32 6.89 6.95 6.94 5.75 7.54 6.50 6.56 7.50 6.43 6.96 7.97 6.81 8.51 7.40 7.76 6.45 7.66 7.98 6.94 6.45 6.89 6.24 6.45 7.74 
Gemcitabine Pyrimidine 6.74 5.62 4.00 8.00 5.20 4.00 7.25 4.00 7.18 5.15 4.71 5.75 8.00 4.63 8.00 4.00 6.16 7.83 8.00 4.19 6.56 7.24 5.60 5.85 4.09 6.17 4.45 4.00 8.00 5.38 6.18 7.57 4.00 8.00 6.68 7.70 4.00 6.77 6.65 4.00 4.06 6.76 4.86 5.82 7.27 
Cladribine Pyrimidine 4.00 4.00 4.00 5.41 4.05 4.60 4.73 4.00 4.83 4.23 4.00 4.68 6.30 4.00 4.86 4.00 4.00 5.85 5.45 4.00 4.86 5.30 4.00 4.00 4.11 4.51 4.00 4.00 6.88 4.00 4.00 5.56 4.00 6.52 4.43 5.42 4.00 4.46 4.56 4.00 4.00 6.41 4.00 4.00 4.24 
Cytarabine Pyrimidine 4.00 4.00 4.00 6.40 4.00 4.00 5.02 4.00 4.00 4.00 4.00 4.54 6.22 4.00 4.00 4.00 4.00 5.22 5.41 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 6.41 4.00 4.00 6.38 4.00 6.56 5.68 5.76 4.00 5.96 5.60 4.00 4.00 7.32 4.00 5.58 4.00 
Etoposide Topo II 4.88 5.48 4.39 6.15 4.66 4.00 5.42 4.68 5.93 4.48 5.11 4.72 5.62 4.86 5.56 4.60 4.92 5.80 5.70 5.05 4.85 5.35 5.35 5.09 4.67 5.79 4.59 4.51 5.43 4.22 4.96 5.55 5.22 6.23 5.80 5.90 4.72 6.11 6.13 5.13 4.41 8.00 4.73 5.10 5.79 
Amsacrine Topo II 5.20 5.78 5.29 6.56 5.25 4.89 5.69 4.93 5.97 5.14 6.56 5.70 6.41 5.56 6.66 5.47 5.77 6.58 6.61 5.43 5.90 5.98 5.71 5.46 5.30 6.24 5.01 4.96 6.43 5.34 5.75 6.55 5.50 6.98 6.44 6.68 4.91 6.53 6.30 5.71 4.99 6.60 5.06 5.57 6.29 
2-Dimethylaminoetoposide Topo II 4.67 4.82 4.02 6.02 4.48 4.00 5.03 4.00 5.05 4.89 5.74 4.71 5.56 4.66 5.70 4.54 4.73 5.75 5.84 4.57 5.20 5.54 4.75 4.66 4.70 5.63 4.57 4.37 5.67 4.29 4.97 5.75 5.05 5.99 5.72 6.14 4.12 5.94 5.17 4.78 4.36 6.25 4.57 4.80 5.75 
NK109 Topo II 5.69 5.88 5.27 6.37 6.04 5.49 6.31 5.56 6.30 5.57 6.08 5.81 6.56 5.96 6.72 5.85 6.05 6.83 6.77 5.84 6.24 6.39 5.92 6.09 6.02 6.66 5.88 5.76 6.51 5.62 6.58 6.92 6.29 6.90 6.66 6.78 5.95 6.70 6.47 6.63 5.68 7.27 5.79 5.91 6.86 
MMC DNA alkylator 5.90 6.68 5.68 6.99 5.14 5.46 6.40 5.50 5.42 5.49 5.74 6.69 6.56 5.04 7.09 5.63 5.73 6.15 6.31 5.38 5.32 6.20 5.50 5.99 4.93 5.00 5.33 5.10 7.09 5.56 5.75 6.17 5.74 6.45 5.99 7.28 5.58 6.27 6.23 5.86 5.75 5.56 5.32 6.03 5.86 
Methotrexate DHFR 7.11 5.19 4.00 7.53 4.00 4.00 7.53 5.25 4.00 4.00 4.00 4.00 7.47 4.00 6.11 4.00 6.12 6.64 6.83 4.00 6.71 4.06 4.00 5.13 7.27 7.04 4.00 4.00 7.15 4.00 7.06 7.04 7.49 7.37 7.33 7.32 4.00 7.38 7.53 7.81 4.00 6.66 4.00 4.00 4.00 
Radicicol HSP90/Tyr kinase 5.55 5.80 5.17 7.28 6.55 5.19 6.13 5.28 7.43 5.39 6.18 6.62 7.87 7.08 6.43 6.16 6.46 6.63 6.83 6.03 5.52 5.61 5.94 5.68 6.96 6.59 5.88 5.66 6.44 6.15 6.40 6.89 6.00 6.63 7.42 6.08 5.71 7.63 7.07 6.78 6.80 6.80 5.76 6.38 6.74 
Vinblastine Tubulin 9.22 9.76 9.22 9.68 8.67 9.17 9.77 9.13 9.15 6.00 7.58 7.99 8.18 6.50 9.30 7.73 9.35 9.73 9.20 7.22 6.00 9.51 9.11 9.66 6.17 9.62 7.60 9.64 9.04 9.25 8.58 9.88 9.37 9.76 9.85 9.53 8.20 9.85 9.69 9.80 9.28 9.71 7.04 8.12 8.33 
Vincristine Tubulin 8.77 9.72 9.29 9.42 8.67 9.12 9.57 9.31 9.22 6.00 8.41 6.20 7.93 6.00 7.70 6.00 8.52 8.76 8.40 6.00 6.00 8.27 8.38 9.11 6.37 9.36 8.60 8.58 8.42 9.13 8.12 9.30 8.91 9.36 9.61 8.94 7.12 9.70 9.24 9.35 9.41 10.00 6.00 7.46 8.20 
Vinorelbine Tubulin 8.45 9.23 8.51 8.85 8.23 8.33 9.35 8.93 8.41 6.00 8.16 6.00 7.98 6.00 8.15 6.00 8.43 8.75 8.28 7.05 6.00 8.51 8.65 9.21 6.00 8.60 8.51 8.59 8.42 8.53 7.96 9.22 8.37 8.89 8.83 8.87 7.13 9.32 8.86 8.87 8.58 9.79 6.00 8.25 8.64 
Paclitaxel Tubulin 7.30 8.43 7.94 7.72 7.37 7.38 8.20 7.53 7.90 6.00 7.05 6.59 7.35 6.84 7.41 6.48 7.44 7.50 7.27 6.00 6.73 7.80 8.22 7.94 6.87 7.68 7.50 7.48 7.89 7.16 6.77 8.15 7.70 8.09 7.86 8.15 6.49 8.07 7.74 7.96 8.03 8.29 6.52 7.79 7.52 
Docetaxel Tubulin 8.41 8.98 8.23 8.52 7.88 8.18 8.82 8.19 8.56 6.00 7.15 8.28 8.08 7.11 7.83 6.80 8.23 8.09 8.08 6.00 6.14 8.50 8.54 8.50 7.05 8.06 8.10 8.32 8.47 7.71 6.93 8.85 8.19 9.08 8.50 8.51 7.21 8.86 8.63 8.47 8.49 8.46 7.33 8.68 8.27 
Dolastatine 10 Tubulin 9.15 10.83 11.19 10.26 9.07 10.02 10.74 9.44 9.95 8.00 9.46 8.67 10.42 8.94 10.71 9.50 10.12 10.19 9.94 8.60 8.00 10.30 9.68 10.61 9.41 9.56 10.27 10.18 9.75 10.29 10.51 10.60 9.23 10.42 10.53 10.35 8.89 10.69 10.50 10.44 10.13 11.86 8.69 10.09 10.26 
Colchicine Tubulin 6.06 8.68 6.33 6.48 7.24 7.58 8.48 7.89 6.64 5.00 7.84 6.59 7.16 5.40 7.25 6.43 7.62 7.77 7.39 5.54 5.00 7.50 7.45 8.17 7.76 7.99 7.28 7.90 7.75 7.51 7.34 7.78 7.65 7.70 8.69 7.53 5.98 8.59 8.19 8.34 7.45 8.74 6.05 7.56 7.84 
E7010 Tubulin 4.37 6.56 4.00 6.14 5.07 5.38 6.69 5.71 6.29 5.50 6.04 4.72 6.28 4.62 6.38 6.23 6.35 6.47 6.35 4.79 4.00 6.50 6.44 6.50 6.06 6.21 6.26 6.35 6.02 6.15 6.39 6.69 6.08 6.69 6.67 6.40 4.37 6.69 6.47 6.64 6.27 6.88 4.51 5.50 5.36 
Melphalan DNA cross-linker 4.20 4.92 4.42 5.09 4.33 4.67 4.04 4.66 4.38 4.08 4.45 4.57 4.76 4.47 4.62 4.00 4.44 4.59 4.81 4.03 4.39 4.40 4.84 4.86 4.47 4.70 4.19 4.00 4.79 4.36 4.55 4.59 4.72 5.18 5.26 5.32 4.56 5.34 5.27 4.00 5.00 4.62 4.15 4.73 4.67 
Leptomycin B Cell cycle inhibitor 9.35 9.64 9.33 9.44 8.91 9.59 9.47 9.63 9.26 8.96 9.78 9.74 9.67 9.32 9.44 9.19 9.10 9.31 9.37 9.00 9.29 9.51 9.54 9.66 9.45 9.44 9.36 9.25 9.45 9.50 9.15 9.48 9.57 9.81 9.69 9.54 9.12 9.64 9.53 8.66 9.16 9.71 8.82 9.76 9.49 
Carboplatin DNA cross-linker 4.00 4.34 4.12 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.18 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.53 4.00 4.25 4.00 4.00 4.00 4.00 4.00 4.14 4.00 4.00 4.24 4.97 4.00 4.36 4.16 4.00 4.00 4.62 4.00 4.00 4.26 
Cisplatin DNA cross-linker 4.90 5.69 5.65 5.09 4.56 4.72 5.52 4.63 4.56 5.35 4.71 5.39 5.53 5.32 5.51 4.75 5.63 5.36 5.45 5.26 4.73 4.94 5.41 5.86 4.78 5.61 5.07 4.66 5.47 4.48 5.35 5.46 4.75 5.12 5.60 6.52 4.80 5.64 5.55 4.74 5.71 5.79 5.43 5.57 5.51 
4-Hydroperoxycyclophosphamide DNA alkylator 4.78 4.85 5.41 5.58 4.68 4.78 4.54 4.74 4.86 5.18 4.76 4.78 4.92 4.74 4.88 4.65 4.84 4.87 5.04 4.82 4.69 4.90 4.76 5.30 4.37 4.77 4.81 4.92 5.13 4.76 4.85 4.81 4.80 5.30 5.25 5.33 4.78 5.50 5.44 4.70 4.68 5.17 4.61 4.66 4.78 
6-Mercaptopurine Purine 5.41 4.73 4.15 5.88 5.17 5.11 4.50 5.02 6.00 4.27 4.05 4.50 5.01 4.10 5.12 4.42 4.00 4.17 4.49 4.90 5.29 4.58 4.82 5.10 4.21 5.58 4.67 5.21 5.39 5.86 4.45 5.21 5.47 5.54 5.97 5.03 5.19 5.90 5.86 4.95 4.55 4.85 4.00 4.00 4.00 
6-Thioguanine Purine 4.59 5.85 5.40 5.86 5.80 5.92 5.55 5.91 5.81 4.53 5.21 5.66 5.08 4.57 5.23 5.37 4.70 4.22 5.14 6.04 5.76 5.18 5.92 6.14 6.18 6.13 5.49 5.46 5.66 5.74 5.83 5.57 5.83 6.21 6.53 5.36 5.50 6.54 5.61 5.79 5.92 6.10 4.00 4.46 4.36 
L-Asparaginase Protein synthesis 6.55 6.63 4.00 6.43 6.01 6.03 7.20 6.18 6.10 5.49 6.07 6.36 6.40 4.78 8.00 6.49 4.00 6.91 6.63 4.00 6.35 8.00 6.61 4.42 6.32 6.41 6.64 6.54 6.65 6.91 5.30 6.70 5.78 6.72 6.34 6.51 6.63 6.47 6.93 6.51 4.94 6.52 4.00 5.56 4.00 
Estramustine Estradiol 4.09 4.51 4.00 4.00 4.66 4.85 4.56 4.31 4.17 4.74 4.00 4.73 4.00 4.00 4.27 4.24 4.05 4.37 4.03 4.10 4.14 4.18 4.09 4.14 4.21 4.26 4.00 4.00 4.20 4.72 4.29 4.45 4.34 4.20 5.11 4.48 4.08 5.03 4.74 4.42 4.02 4.79 4.59 4.95 4.76 
IFN-α Biological response 4.00 7.71 4.00 4.00 4.23 4.00 4.00 4.00 4.00 4.00 4.00 5.02 4.00 4.00 4.20 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.51 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.51 4.20 4.62 4.62 4.16 
IFN-β Biological response 4.00 8.00 4.00 4.00 6.40 4.23 7.08 4.00 4.00 4.00 4.00 4.56 4.00 4.00 7.15 6.17 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 6.02 4.93 4.77 6.28 6.54 
IFN-γ Biological response 7.69 7.93 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 7.93 4.00 4.07 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 5.06 4.00 
Drug nameTarget/mode of actionBreast
Liver
Stomach
HBC-4BSY1HBC-5MCF-7MDA-MB-231KPL-3CKPL-4KPL-1T-47DHBC-9ZR-75-1HBC-8HepG2Hep3BLi-7PLC/PRF/5HuH7HLEHLFHuH6RBESSP-25HuL-1JHH-1St-4MKN1MKN7MKN28MKN45MKN74GCIYGT3 TKBHGC27AZ5214-1STNUGC -3NUGC -3/5-FUHSC-42AGSKWS-1TGS-11OKIBAISt-1ALFAOTO
Aclarubicin DNA/RNA synthesis 7.04 8.69 7.92 7.86 7.83 7.11 7.63 7.95 7.39 7.08 8.03 7.93 8.13 7.77 7.39 7.68 8.29 7.49 7.86 7.70 7.87 7.39 7.97 8.23 7.88 8.09 7.73 7.25 8.59 7.43 8.00 7.86 7.13 8.49 7.96 9.04 7.51 8.21 8.27 7.96 8.31 7.20 7.19 8.54 7.57 
Oxaliplatin DNA cross-linker 5.79 5.75 5.40 5.69 4.75 5.04 5.20 4.78 5.17 4.10 5.08 6.17 7.07 5.39 5.78 5.61 6.44 4.90 4.75 5.60 5.19 4.58 6.04 6.01 4.75 5.04 4.42 4.58 6.84 4.93 5.71 5.31 5.10 6.16 5.17 6.18 5.23 5.98 5.58 6.26 7.02 5.85 5.14 5.46 4.78 
Actinomycin D RNA synthesis 9.20 9.10 8.85 9.45 8.71 8.90 9.05 9.04 8.89 8.24 8.98 9.60 9.03 8.61 8.24 8.04 8.99 8.13 8.45 8.75 8.25 8.47 8.78 9.00 7.99 8.74 8.77 9.02 9.39 9.20 8.24 9.12 8.76 9.55 8.80 8.85 8.56 9.32 8.99 9.22 9.55 9.35 8.77 9.39 8.88 
HCFU Pyrimidine 4.36 5.17 4.44 5.13 4.57 4.65 5.55 4.41 4.97 4.22 4.68 4.84 5.28 4.80 4.79 4.56 4.99 4.67 4.70 4.50 4.92 4.69 4.87 4.63 4.17 4.70 4.82 4.77 5.56 4.86 4.77 5.09 4.74 5.21 4.84 4.74 4.36 4.89 5.00 4.71 4.27 5.10 4.15 4.23 4.44 
5-FU Pyrimidine 4.43 4.87 4.40 5.12 4.18 4.00 5.23 4.00 4.13 4.00 4.70 5.11 5.27 4.20 4.26 4.21 5.08 4.00 4.19 4.00 4.60 4.00 5.29 4.72 4.35 4.40 4.26 4.27 5.46 4.22 4.60 5.09 4.34 5.12 4.04 4.67 4.00 4.40 5.02 4.50 4.06 6.38 4.00 4.42 4.09 
Doxifluridine Pyrimidine 4.00 4.42 4.00 4.00 4.00 4.00 4.09 4.00 4.00 4.00 4.14 4.19 4.49 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.04 4.00 4.00 4.00 4.01 4.00 4.20 4.00 4.00 4.00 4.00 4.00 4.00 4.02 4.00 4.00 4.26 4.00 4.00 4.18 4.00 4.00 4.00 
E7070 Cell cycle inhibitor 4.50 6.20 4.22 4.50 4.35 4.94 5.01 4.74 4.69 4.00 4.38 4.98 5.47 4.99 4.77 4.44 5.36 4.61 4.43 4.74 5.09 4.29 4.29 4.87 4.43 6.03 4.90 5.48 4.55 5.20 5.04 4.82 5.69 6.02 4.88 5.75 4.39 4.81 4.46 5.25 4.96 6.05 4.83 6.69 4.97 
Tamoxifen Estrogen receptor 4.95 5.42 5.01 5.04 4.90 5.14 5.49 4.93 5.31 4.90 4.95 5.53 5.45 5.30 5.23 4.79 5.09 5.02 4.97 5.38 4.90 5.11 4.87 4.97 4.95 4.89 5.44 5.23 5.13 5.67 4.92 5.19 5.25 5.11 4.87 5.06 4.86 4.89 5.59 4.93 5.20 5.58 4.93 5.43 5.13 
Toremifene Estrogen receptor 4.81 5.12 4.87 4.96 4.85 4.93 5.13 4.88 5.17 4.89 4.88 4.86 5.06 4.97 4.92 4.82 4.99 5.09 4.91 4.95 4.92 5.00 4.80 5.10 4.81 4.92 4.90 4.82 4.93 5.23 4.85 4.92 5.07 5.09 4.87 4.96 4.85 4.88 5.00 4.93 5.07 5.58 4.88 5.50 5.24 
MS-247 DNA synthesis 6.08 6.79 5.32 6.78 5.98 6.09 6.16 5.86 6.63 6.42 6.88 6.71 6.33 5.84 6.35 5.23 6.02 6.58 6.42 5.82 5.66 6.37 5.67 6.82 5.66 5.72 6.27 5.59 7.32 6.62 5.71 6.88 6.76 7.58 7.09 6.62 5.64 7.11 7.01 6.74 6.67 6.20 5.70 5.70 5.63 
Daunorubicin DNA synthesis/topo II 6.96 7.34 6.82 7.68 6.83 6.77 7.25 6.84 7.41 6.92 7.39 7.97 7.48 7.10 6.83 6.39 7.29 7.55 7.49 6.98 7.18 6.73 7.08 7.51 6.60 7.30 6.98 7.03 7.66 6.88 6.79 7.55 7.17 7.98 7.18 7.74 6.85 7.57 7.42 6.99 6.93 7.59 6.37 6.94 6.80 
Doxorubicin DNA synthesis/topo II 7.13 7.26 6.85 7.58 6.66 6.74 7.38 6.76 7.36 6.94 7.12 7.85 7.29 6.77 6.88 5.83 7.04 7.39 7.25 6.87 6.89 6.68 6.89 7.31 6.39 7.45 6.79 6.71 7.32 6.70 6.39 7.14 6.86 7.87 6.68 7.66 6.47 7.33 7.53 6.91 6.90 8.00 6.01 6.34 6.54 
Epirubicin DNA synthesis/topo II 6.08 6.90 6.59 7.08 6.42 6.50 7.03 6.83 7.26 6.73 7.90 7.19 7.33 6.86 6.87 6.29 7.31 7.21 7.25 6.91 6.84 6.73 6.74 7.03 7.21 7.53 6.85 6.60 7.35 6.60 6.53 7.10 6.71 8.00 7.02 7.68 6.13 7.61 8.02 7.12 6.91 7.12 5.99 7.00 6.51 
Mitoxantrone DNA synthesis 6.28 7.12 6.00 8.06 6.50 6.40 6.83 6.38 7.11 6.96 8.02 7.44 7.95 6.51 7.88 6.51 6.76 7.60 7.67 6.71 7.37 7.59 6.11 7.15 6.82 7.52 6.57 6.52 7.79 6.68 6.87 7.82 6.83 8.79 7.38 7.59 6.18 7.70 7.75 7.21 6.74 8.56 5.76 6.14 6.37 
Pirarubicin DNA synthesis/topo II 8.97 9.00 8.34 9.00 8.47 8.62 9.00 8.39 9.00 8.22 9.00 9.00 9.00 8.58 9.00 8.26 9.00 9.00 9.00 8.59 8.98 9.00 8.95 9.00 8.31 8.97 8.55 8.57 9.00 8.53 8.81 9.00 8.56 9.00 8.86 9.00 8.65 9.00 9.00 8.99 8.58 8.81 8.16 8.68 8.57 
Topotecan Topo I 5.84 6.57 5.10 8.00 5.55 6.37 6.71 5.90 7.51 6.18 7.20 7.61 7.93 5.81 7.70 5.64 6.07 7.73 7.73 5.72 6.83 6.74 5.30 6.99 7.21 6.27 5.54 5.81 8.00 5.62 6.61 7.83 5.64 7.74 8.00 7.68 5.82 8.00 7.54 6.07 6.39 6.10 6.70 6.90 6.85 
SN-38 Topo I 7.98 7.52 5.56 8.56 6.12 6.75 7.40 6.60 8.25 6.13 7.92 7.75 8.43 6.37 8.21 6.03 6.75 8.28 8.31 5.91 7.05 7.47 5.69 7.74 6.83 6.63 6.16 6.16 8.71 6.17 6.89 8.49 6.04 8.49 8.78 8.28 6.31 8.61 8.70 6.81 6.66 7.07 7.29 7.46 7.28 
Camptothecin Topo I 5.92 6.57 6.04 7.63 5.86 6.67 6.60 6.70 7.12 5.80 7.21 6.92 7.44 6.19 7.48 5.86 6.35 7.42 7.53 6.10 6.69 6.79 6.16 6.92 7.13 6.39 5.82 5.50 7.99 5.62 6.81 7.53 5.49 7.61 7.75 7.73 6.00 7.76 7.23 6.36 6.64 6.81 6.43 6.72 6.96 
Bleomycin DNA synthesis 4.81 4.89 4.00 4.48 4.00 4.00 5.59 4.00 5.46 4.46 4.22 4.37 6.02 4.38 5.66 4.00 4.85 6.04 6.59 4.15 4.73 4.97 5.10 4.94 4.00 4.61 4.03 4.00 4.54 4.22 4.00 6.21 4.22 7.18 6.03 4.75 4.00 5.66 5.19 4.00 4.00 5.55 4.00 4.81 4.58 
Peplomycin DNA synthesis 4.90 5.84 4.00 5.22 4.27 4.61 6.29 4.08 5.37 4.52 4.72 5.25 6.73 4.72 6.40 4.45 5.46 5.86 6.56 4.01 5.12 5.83 5.35 5.34 4.00 4.80 4.56 4.09 5.18 4.82 4.39 5.96 4.68 7.32 6.16 4.92 4.05 6.00 5.82 4.65 4.08 5.92 4.23 5.04 4.78 
Neocarzinostatin DNA synthesis 7.35 8.00 6.03 8.17 6.55 6.42 7.61 6.18 7.26 7.06 7.26 8.10 8.22 6.72 7.81 6.34 6.92 7.60 7.80 6.57 7.27 7.53 6.67 7.09 6.17 6.92 6.58 6.47 8.38 7.19 6.95 7.74 6.92 8.58 7.59 8.00 6.54 7.89 7.78 6.84 6.60 7.05 6.54 6.74 7.24 
Irinotecan Topo I 4.86 5.09 4.00 5.46 4.28 4.30 4.91 4.11 5.21 4.15 4.47 5.24 5.18 4.36 5.61 4.00 4.33 5.25 5.13 4.11 4.37 4.64 4.05 4.78 4.00 4.41 4.29 4.02 5.41 4.26 4.44 5.24 4.00 5.58 5.39 5.41 4.06 5.48 5.50 4.25 4.58 4.64 4.42 4.56 4.71 
TAS103 Topo 6.81 7.22 6.37 7.66 6.57 6.45 7.20 6.17 7.25 6.16 7.13 7.60 7.56 6.57 7.68 6.64 6.95 7.81 7.87 6.55 7.32 6.89 6.95 6.94 5.75 7.54 6.50 6.56 7.50 6.43 6.96 7.97 6.81 8.51 7.40 7.76 6.45 7.66 7.98 6.94 6.45 6.89 6.24 6.45 7.74 
Gemcitabine Pyrimidine 6.74 5.62 4.00 8.00 5.20 4.00 7.25 4.00 7.18 5.15 4.71 5.75 8.00 4.63 8.00 4.00 6.16 7.83 8.00 4.19 6.56 7.24 5.60 5.85 4.09 6.17 4.45 4.00 8.00 5.38 6.18 7.57 4.00 8.00 6.68 7.70 4.00 6.77 6.65 4.00 4.06 6.76 4.86 5.82 7.27 
Cladribine Pyrimidine 4.00 4.00 4.00 5.41 4.05 4.60 4.73 4.00 4.83 4.23 4.00 4.68 6.30 4.00 4.86 4.00 4.00 5.85 5.45 4.00 4.86 5.30 4.00 4.00 4.11 4.51 4.00 4.00 6.88 4.00 4.00 5.56 4.00 6.52 4.43 5.42 4.00 4.46 4.56 4.00 4.00 6.41 4.00 4.00 4.24 
Cytarabine Pyrimidine 4.00 4.00 4.00 6.40 4.00 4.00 5.02 4.00 4.00 4.00 4.00 4.54 6.22 4.00 4.00 4.00 4.00 5.22 5.41 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 6.41 4.00 4.00 6.38 4.00 6.56 5.68 5.76 4.00 5.96 5.60 4.00 4.00 7.32 4.00 5.58 4.00 
Etoposide Topo II 4.88 5.48 4.39 6.15 4.66 4.00 5.42 4.68 5.93 4.48 5.11 4.72 5.62 4.86 5.56 4.60 4.92 5.80 5.70 5.05 4.85 5.35 5.35 5.09 4.67 5.79 4.59 4.51 5.43 4.22 4.96 5.55 5.22 6.23 5.80 5.90 4.72 6.11 6.13 5.13 4.41 8.00 4.73 5.10 5.79 
Amsacrine Topo II 5.20 5.78 5.29 6.56 5.25 4.89 5.69 4.93 5.97 5.14 6.56 5.70 6.41 5.56 6.66 5.47 5.77 6.58 6.61 5.43 5.90 5.98 5.71 5.46 5.30 6.24 5.01 4.96 6.43 5.34 5.75 6.55 5.50 6.98 6.44 6.68 4.91 6.53 6.30 5.71 4.99 6.60 5.06 5.57 6.29 
2-Dimethylaminoetoposide Topo II 4.67 4.82 4.02 6.02 4.48 4.00 5.03 4.00 5.05 4.89 5.74 4.71 5.56 4.66 5.70 4.54 4.73 5.75 5.84 4.57 5.20 5.54 4.75 4.66 4.70 5.63 4.57 4.37 5.67 4.29 4.97 5.75 5.05 5.99 5.72 6.14 4.12 5.94 5.17 4.78 4.36 6.25 4.57 4.80 5.75 
NK109 Topo II 5.69 5.88 5.27 6.37 6.04 5.49 6.31 5.56 6.30 5.57 6.08 5.81 6.56 5.96 6.72 5.85 6.05 6.83 6.77 5.84 6.24 6.39 5.92 6.09 6.02 6.66 5.88 5.76 6.51 5.62 6.58 6.92 6.29 6.90 6.66 6.78 5.95 6.70 6.47 6.63 5.68 7.27 5.79 5.91 6.86 
MMC DNA alkylator 5.90 6.68 5.68 6.99 5.14 5.46 6.40 5.50 5.42 5.49 5.74 6.69 6.56 5.04 7.09 5.63 5.73 6.15 6.31 5.38 5.32 6.20 5.50 5.99 4.93 5.00 5.33 5.10 7.09 5.56 5.75 6.17 5.74 6.45 5.99 7.28 5.58 6.27 6.23 5.86 5.75 5.56 5.32 6.03 5.86 
Methotrexate DHFR 7.11 5.19 4.00 7.53 4.00 4.00 7.53 5.25 4.00 4.00 4.00 4.00 7.47 4.00 6.11 4.00 6.12 6.64 6.83 4.00 6.71 4.06 4.00 5.13 7.27 7.04 4.00 4.00 7.15 4.00 7.06 7.04 7.49 7.37 7.33 7.32 4.00 7.38 7.53 7.81 4.00 6.66 4.00 4.00 4.00 
Radicicol HSP90/Tyr kinase 5.55 5.80 5.17 7.28 6.55 5.19 6.13 5.28 7.43 5.39 6.18 6.62 7.87 7.08 6.43 6.16 6.46 6.63 6.83 6.03 5.52 5.61 5.94 5.68 6.96 6.59 5.88 5.66 6.44 6.15 6.40 6.89 6.00 6.63 7.42 6.08 5.71 7.63 7.07 6.78 6.80 6.80 5.76 6.38 6.74 
Vinblastine Tubulin 9.22 9.76 9.22 9.68 8.67 9.17 9.77 9.13 9.15 6.00 7.58 7.99 8.18 6.50 9.30 7.73 9.35 9.73 9.20 7.22 6.00 9.51 9.11 9.66 6.17 9.62 7.60 9.64 9.04 9.25 8.58 9.88 9.37 9.76 9.85 9.53 8.20 9.85 9.69 9.80 9.28 9.71 7.04 8.12 8.33 
Vincristine Tubulin 8.77 9.72 9.29 9.42 8.67 9.12 9.57 9.31 9.22 6.00 8.41 6.20 7.93 6.00 7.70 6.00 8.52 8.76 8.40 6.00 6.00 8.27 8.38 9.11 6.37 9.36 8.60 8.58 8.42 9.13 8.12 9.30 8.91 9.36 9.61 8.94 7.12 9.70 9.24 9.35 9.41 10.00 6.00 7.46 8.20 
Vinorelbine Tubulin 8.45 9.23 8.51 8.85 8.23 8.33 9.35 8.93 8.41 6.00 8.16 6.00 7.98 6.00 8.15 6.00 8.43 8.75 8.28 7.05 6.00 8.51 8.65 9.21 6.00 8.60 8.51 8.59 8.42 8.53 7.96 9.22 8.37 8.89 8.83 8.87 7.13 9.32 8.86 8.87 8.58 9.79 6.00 8.25 8.64 
Paclitaxel Tubulin 7.30 8.43 7.94 7.72 7.37 7.38 8.20 7.53 7.90 6.00 7.05 6.59 7.35 6.84 7.41 6.48 7.44 7.50 7.27 6.00 6.73 7.80 8.22 7.94 6.87 7.68 7.50 7.48 7.89 7.16 6.77 8.15 7.70 8.09 7.86 8.15 6.49 8.07 7.74 7.96 8.03 8.29 6.52 7.79 7.52 
Docetaxel Tubulin 8.41 8.98 8.23 8.52 7.88 8.18 8.82 8.19 8.56 6.00 7.15 8.28 8.08 7.11 7.83 6.80 8.23 8.09 8.08 6.00 6.14 8.50 8.54 8.50 7.05 8.06 8.10 8.32 8.47 7.71 6.93 8.85 8.19 9.08 8.50 8.51 7.21 8.86 8.63 8.47 8.49 8.46 7.33 8.68 8.27 
Dolastatine 10 Tubulin 9.15 10.83 11.19 10.26 9.07 10.02 10.74 9.44 9.95 8.00 9.46 8.67 10.42 8.94 10.71 9.50 10.12 10.19 9.94 8.60 8.00 10.30 9.68 10.61 9.41 9.56 10.27 10.18 9.75 10.29 10.51 10.60 9.23 10.42 10.53 10.35 8.89 10.69 10.50 10.44 10.13 11.86 8.69 10.09 10.26 
Colchicine Tubulin 6.06 8.68 6.33 6.48 7.24 7.58 8.48 7.89 6.64 5.00 7.84 6.59 7.16 5.40 7.25 6.43 7.62 7.77 7.39 5.54 5.00 7.50 7.45 8.17 7.76 7.99 7.28 7.90 7.75 7.51 7.34 7.78 7.65 7.70 8.69 7.53 5.98 8.59 8.19 8.34 7.45 8.74 6.05 7.56 7.84 
E7010 Tubulin 4.37 6.56 4.00 6.14 5.07 5.38 6.69 5.71 6.29 5.50 6.04 4.72 6.28 4.62 6.38 6.23 6.35 6.47 6.35 4.79 4.00 6.50 6.44 6.50 6.06 6.21 6.26 6.35 6.02 6.15 6.39 6.69 6.08 6.69 6.67 6.40 4.37 6.69 6.47 6.64 6.27 6.88 4.51 5.50 5.36 
Melphalan DNA cross-linker 4.20 4.92 4.42 5.09 4.33 4.67 4.04 4.66 4.38 4.08 4.45 4.57 4.76 4.47 4.62 4.00 4.44 4.59 4.81 4.03 4.39 4.40 4.84 4.86 4.47 4.70 4.19 4.00 4.79 4.36 4.55 4.59 4.72 5.18 5.26 5.32 4.56 5.34 5.27 4.00 5.00 4.62 4.15 4.73 4.67 
Leptomycin B Cell cycle inhibitor 9.35 9.64 9.33 9.44 8.91 9.59 9.47 9.63 9.26 8.96 9.78 9.74 9.67 9.32 9.44 9.19 9.10 9.31 9.37 9.00 9.29 9.51 9.54 9.66 9.45 9.44 9.36 9.25 9.45 9.50 9.15 9.48 9.57 9.81 9.69 9.54 9.12 9.64 9.53 8.66 9.16 9.71 8.82 9.76 9.49 
Carboplatin DNA cross-linker 4.00 4.34 4.12 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.18 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.53 4.00 4.25 4.00 4.00 4.00 4.00 4.00 4.14 4.00 4.00 4.24 4.97 4.00 4.36 4.16 4.00 4.00 4.62 4.00 4.00 4.26 
Cisplatin DNA cross-linker 4.90 5.69 5.65 5.09 4.56 4.72 5.52 4.63 4.56 5.35 4.71 5.39 5.53 5.32 5.51 4.75 5.63 5.36 5.45 5.26 4.73 4.94 5.41 5.86 4.78 5.61 5.07 4.66 5.47 4.48 5.35 5.46 4.75 5.12 5.60 6.52 4.80 5.64 5.55 4.74 5.71 5.79 5.43 5.57 5.51 
4-Hydroperoxycyclophosphamide DNA alkylator 4.78 4.85 5.41 5.58 4.68 4.78 4.54 4.74 4.86 5.18 4.76 4.78 4.92 4.74 4.88 4.65 4.84 4.87 5.04 4.82 4.69 4.90 4.76 5.30 4.37 4.77 4.81 4.92 5.13 4.76 4.85 4.81 4.80 5.30 5.25 5.33 4.78 5.50 5.44 4.70 4.68 5.17 4.61 4.66 4.78 
6-Mercaptopurine Purine 5.41 4.73 4.15 5.88 5.17 5.11 4.50 5.02 6.00 4.27 4.05 4.50 5.01 4.10 5.12 4.42 4.00 4.17 4.49 4.90 5.29 4.58 4.82 5.10 4.21 5.58 4.67 5.21 5.39 5.86 4.45 5.21 5.47 5.54 5.97 5.03 5.19 5.90 5.86 4.95 4.55 4.85 4.00 4.00 4.00 
6-Thioguanine Purine 4.59 5.85 5.40 5.86 5.80 5.92 5.55 5.91 5.81 4.53 5.21 5.66 5.08 4.57 5.23 5.37 4.70 4.22 5.14 6.04 5.76 5.18 5.92 6.14 6.18 6.13 5.49 5.46 5.66 5.74 5.83 5.57 5.83 6.21 6.53 5.36 5.50 6.54 5.61 5.79 5.92 6.10 4.00 4.46 4.36 
L-Asparaginase Protein synthesis 6.55 6.63 4.00 6.43 6.01 6.03 7.20 6.18 6.10 5.49 6.07 6.36 6.40 4.78 8.00 6.49 4.00 6.91 6.63 4.00 6.35 8.00 6.61 4.42 6.32 6.41 6.64 6.54 6.65 6.91 5.30 6.70 5.78 6.72 6.34 6.51 6.63 6.47 6.93 6.51 4.94 6.52 4.00 5.56 4.00 
Estramustine Estradiol 4.09 4.51 4.00 4.00 4.66 4.85 4.56 4.31 4.17 4.74 4.00 4.73 4.00 4.00 4.27 4.24 4.05 4.37 4.03 4.10 4.14 4.18 4.09 4.14 4.21 4.26 4.00 4.00 4.20 4.72 4.29 4.45 4.34 4.20 5.11 4.48 4.08 5.03 4.74 4.42 4.02 4.79 4.59 4.95 4.76 
IFN-α Biological response 4.00 7.71 4.00 4.00 4.23 4.00 4.00 4.00 4.00 4.00 4.00 5.02 4.00 4.00 4.20 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.51 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.51 4.20 4.62 4.62 4.16 
IFN-β Biological response 4.00 8.00 4.00 4.00 6.40 4.23 7.08 4.00 4.00 4.00 4.00 4.56 4.00 4.00 7.15 6.17 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 6.02 4.93 4.77 6.28 6.54 
IFN-γ Biological response 7.69 7.93 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 7.93 4.00 4.07 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 5.06 4.00 
Figure 1.

Hierarchical clustering of 53 anticancer drugs based on their activity on 45 human cancer cell lines. Hierarchical clustering method was “average linkage method” using Pearson correlation as distance. Fifty-three drugs were classified into several clusters, each consisting of drugs with similar modes of action or targets: (A) 5-FU derivatives, (B) estrogen receptor, (C) DNA synthesis/topo II inhibitors, (D) topo I inhibitors, (E) topo II inhibitors, (F) tubulin binders, and (G) IFN.

Figure 1.

Hierarchical clustering of 53 anticancer drugs based on their activity on 45 human cancer cell lines. Hierarchical clustering method was “average linkage method” using Pearson correlation as distance. Fifty-three drugs were classified into several clusters, each consisting of drugs with similar modes of action or targets: (A) 5-FU derivatives, (B) estrogen receptor, (C) DNA synthesis/topo II inhibitors, (D) topo I inhibitors, (E) topo II inhibitors, (F) tubulin binders, and (G) IFN.

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Classification of 42 Human Cancer Cell Lines According to Gene Expression Profiles

Using a cDNA array, we examined the expression of 3,537 genes in 42 cell lines of JFCR-45. Based on these expression profiles, hierarchical clustering was done. In a few experiments, cell lines derived from the same organ were clustered into a group (Fig. 2). Breast cancer cell lines, except KPL-4, formed one cluster. Liver and stomach cancer cell lines clustered separately from the breast cancer cell lines and formed tissue-specific subclusters. However, four stomach cancer cell lines, AOTO, ISt-1, TGS-11, and HGC27, were intercalated into a cluster of liver cancer cell lines. These results suggested that the established cell lines maintained characteristics of their organ of origin as far as the gene expression profile was concerned.

Figure 2.

Hierarchical clustering of 42 human cancer cell lines based on their gene expression profiles. Gradient color indicates relative level (log2 transformed) of gene expression. Red, high expression of gene (2.0); yellow, normal expression of gene (0.0); green, low expression of gene (−2.0). Red was expressed four times more than yellow. Br, Ga, and Li, breast, stomach, and liver cancer cell lines, respectively. Cell lines with the same tissue of origin tended to form a cluster.

Figure 2.

Hierarchical clustering of 42 human cancer cell lines based on their gene expression profiles. Gradient color indicates relative level (log2 transformed) of gene expression. Red, high expression of gene (2.0); yellow, normal expression of gene (0.0); green, low expression of gene (−2.0). Red was expressed four times more than yellow. Br, Ga, and Li, breast, stomach, and liver cancer cell lines, respectively. Cell lines with the same tissue of origin tended to form a cluster.

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Correlation Analysis between Gene Expression Profiles and Chemosensitivity Profiles

To investigate genes that may be involved in chemosensitivity, we integrated the two databases and did a correlation analysis between gene expression and drug sensitivity. Comprehensive calculations for the Pearson correlation coefficients were done on the expression of 3,537 genes and sensitivity to 53 drugs in 42 cell lines. We selected genes that satisfied the following criteria: showing a P of correlation < 0.05 between the expression of the gene and its sensitivity to a certain drug and being significantly expressed in >50% of the cell lines. We examined the data for the distribution by scatter graph analysis and removed those data showing a highly non-normal distribution. The higher the expression of the gene showing positive correlation, the higher the sensitivity was to the drug (i.e., this gene was a sensitive candidate gene). In contrast, genes that showed a negative correlation with chemosensitivity were resistant candidate genes. Consequently, different sets of genes were extracted with respect to each of the 53 drugs. Table 2 shows sets of genes whose expression was correlated with the sensitivity of 42 cell lines to MMC, paclitaxel, vinorelbine, and SN-38. As for MMC, 20 genes were extracted as sensitive genes and 10 genes were extracted as resistant candidate genes. Some of these genes (such as JUN, EMS1, and NMBR) are related to cell growth, whereas others included various types of genes (such as SOD1, PELP1, SFRS9, etc.). Similarly, many sensitive and resistant candidate genes were extracted with the other drugs tested. We further applied a Pearson correlation analysis to the cell lines originating from the same organ. Genes whose expressions were correlated with the MMC sensitivity in 10 breast cancer, 12 liver cancer, and 20 stomach cancer cell lines are shown in Table 3. As described previously (19, 20), these genes may predict chemosensitivity.

Table 2.

Genes related to the sensitivity to MMC, vinorelbine, paclitaxel, and SN-38 in 42 human cancer cell lines

RankGeneGenbank IDrP
A. MMC     
Sensitive     
    1 SF1 D26121 0.566 0.001 
    2 CBR3 Ab004854 0.486 0.006 
    3 EMS1 M98343 0.480 0.010 
    4 JUN J04111 0.473 0.015 
    5 SFRS9 U30825 0.448 0.010 
    6 NMBR M73482 0.428 0.012 
    7 RBMX Z23064 0.419 0.012 
    8 SOD1 M13267 0.418 0.024 
    9 NOL1 X55504 0.415 0.025 
    10 PELP1 U88153 0.405 0.019 
    11 ARHA L25080 0.404 0.030 
    12 AARS D32050 0.398 0.018 
    13 NME1 X17620 0.398 0.032 
    14 HNRPA2B1 M29065 0.390 0.044 
    15 NME2 L16785 0.378 0.025 
    16 VAT1 U18009 0.376 0.031 
    17 SERPINB10 U35459 0.372 0.028 
    18 KIAA0436 AB007896 0.353 0.041 
    19 DRPLA D31840 0.350 0.049 
    20 MC3R L06155 0.346 0.049 
Resistant     
    1 SPTBN1 M96803 −0.450 0.013 
    2 PET112L AF026851 −0.425 0.027 
    3 CAPN1 X04366 −0.421 0.032 
    4 MEL X56741 −0.414 0.028 
    5 PACE X17094 −0.380 0.035 
    6 DVL2 AF006012 −0.370 0.034 
    7 LOC54543 AJ011007 −0.366 0.022 
    8 PAPOLA X76770 −0.351 0.033 
    9 RPLP2 M17887 −0.345 0.049 
    10 ARF4L L38490 −0.340 0.042 
B. Vinorelbine     
Sensitive     
    1 ARHA L25080 0.534 0.003 
    2 NME2 L16785 0.521 0.001 
    3 VIL2 X51521 0.463 0.015 
    4 YWHAQ X56468 0.450 0.011 
    5 HK1 M75126 0.449 0.016 
    6 SATB1 M97287 0.439 0.006 
    7 CAMLG U18242 0.439 0.007 
    8 CARS L06845 0.433 0.007 
    9 CCNB1 M25753 0.427 0.013 
    10 U2AF1 M96982 0.424 0.022 
    11 PTMA M26708 0.423 0.018 
    12 MLC1SA M31211 0.397 0.022 
    13 NME1 X17620 0.393 0.035 
    14 SARS X91257 0.386 0.032 
    15 CDC20 U05340 0.385 0.029 
    16 PPP4C X70218 0.385 0.039 
    17 TNFAIP3 M59465 0.384 0.023 
    18 EEF1D Z21507 0.384 0.023 
    19 PFKP D25328 0.365 0.028 
    20 ENTPD2 U91510 0.365 0.037 
    21 CCL5 M21121 0.358 0.035 
    22 ACAT1 D90228 0.352 0.048 
    23 IQGAP1 L33075 0.351 0.042 
    24 PAX5 M96944 0.342 0.038 
    25 NRGN Y09689 0.336 0.042 
    26 K-α-1 K00558 0.328 0.048 
    27 NDUFB7 M33374 0.321 0.049 
Resistant     
    1 HOXB1 X16666 −0.600 0.000 
    2 F10 K03194 −0.514 0.002 
    3 GPX2 X53463 −0.509 0.002 
    4 NR1I2 AF061056 −0.498 0.002 
    5 ANXA4 M19383 −0.481 0.005 
    6 PDLIM1 U90878 −0.465 0.006 
    7 LIPC X07228 −0.464 0.004 
    8 SERPINF2 D00174 −0.447 0.004 
    9 HSD17B1 M36263 −0.443 0.014 
    10 MAN2B1 U60266 −0.440 0.008 
    11 LSS D63807 −0.430 0.014 
    12 PIK3CG X83368 −0.415 0.010 
    13 DBN1 U00802 −0.414 0.017 
    14 NDUFA4 U94586 −0.410 0.038 
    15 BDH M93107 −0.399 0.024 
    16 BCL2L1 Z23115 −0.385 0.039 
    17 EEF1B2 X60656 −0.383 0.030 
    18 F2 V00595 −0.382 0.026 
    19 RARA X06614 −0.369 0.029 
    20 ITGB4 X53587 −0.367 0.042 
    21 IMPA1 X66922 −0.367 0.042 
    22 PACE X17094 −0.367 0.042 
    23 AGA M64073 −0.361 0.042 
    24 MVD U49260 −0.353 0.038 
    25 EHHADH L07077 −0.346 0.039 
    26 TFPI2 D29992 −0.343 0.035 
    27 MARCKS M68956 −0.342 0.045 
    28 FGB J00129 −0.334 0.035 
    29 GPD1 L34041 −0.322 0.049 
C. Paclitaxel     
Sensitive     
    1 ADH6 M68895 0.513 0.002 
    2 RAB28 X94703 0.480 0.007 
    3 U2AF1 M96982 0.441 0.017 
    4 GPC1 X54232 0.440 0.013 
HK1 M75126 0.439 0.020 
    6 CARS L06845 0.436 0.006 
    7 TNFAIP3 M59465 0.433 0.009 
    8 K-α-1 K00558 0.418 0.010 
    9 PFKP D25328 0.416 0.012 
    10 GDI2 D13988 0.411 0.033 
    11 VIL2 X51521 0.410 0.034 
    12 RUNX2 AF001450 0.409 0.038 
    13 NME2 L16785 0.407 0.015 
    14 CDC20 U05340 0.395 0.025 
    15 GNAI2 X04828 0.391 0.033 
    16 ARHA L25080 0.381 0.041 
    17 CNR2 X74328 0.378 0.030 
    18 PPP2R2B M64930 0.376 0.026 
    19 SLC6A8 L31409 0.374 0.046 
    20 DDX9 L13848 0.374 0.042 
    21 ACAT1 D90228 0.369 0.038 
    22 PI3 Z18538 0.329 0.047 
Resistant     
    1 NAP1L1 M86667 −0.530 0.004 
    2 HOXB1 X16666 −0.516 0.004 
    3 PACE X17094 −0.507 0.004 
    4 MAN2B1 U60266 −0.486 0.003 
    5 GPX2 X53463 −0.480 0.004 
    6 DBN1 U00802 −0.469 0.006 
    7 ANXA4 M19383 −0.468 0.007 
    8 SERPINF2 D00174 −0.463 0.003 
    9 AGA M64073 −0.444 0.011 
    10 BCL2L1 Z23115 −0.428 0.021 
    11 LIPC X07228 −0.401 0.015 
    12 BDH M93107 −0.393 0.026 
    13 LSS D63807 −0.384 0.030 
    14 PDLIM1 U90878 −0.372 0.033 
    15 ZNF161 D28118 −0.368 0.038 
    16 UBE2E1 X92963 −0.363 0.032 
    17 TLE1 M99435 −0.360 0.039 
    18 RARA X06614 −0.359 0.034 
    19 PTPRN L18983 −0.357 0.035 
    20 APOE M12529 −0.353 0.048 
    21 F10 K03194 −0.348 0.040 
    22 NR1I2 AF061056 −0.342 0.041 
    23 UBE2L3 X92962 −0.332 0.045 
    24 FGB J00129 −0.313 0.049 
D. SN-38     
Sensitive     
    1 EMS1 M98343 0.573 0.001 
    2 JUN J04111 0.564 0.003 
    3 IL-6 X04602 0.514 0.003 
    4 RPL23 X52839 0.495 0.004 
    5 CDKN3 L25876 0.455 0.017 
    6 RPL3 X73460 0.445 0.011 
    7 TFPI J03225 0.442 0.009 
    8 MRPL3 X06323 0.437 0.009 
    9 HLA-C M11886 0.424 0.014 
    10 AARS D32050 0.419 0.012 
    11 ARHGDIA X69550 0.416 0.031 
    12 NOL1 X55504 0.406 0.029 
    13 SF1 D26121 0.394 0.031 
    14 SOD1 M13267 0.389 0.037 
    15 VEGF M32977 0.384 0.043 
    16 EIF2S1 J02645 0.382 0.034 
    17 CDH5 X79981 0.372 0.030 
    18 FOSL1 X16707 0.371 0.047 
    19 IDS M58342 0.366 0.047 
    20 PMVK L77213 0.364 0.044 
    21 PPP2CB X12656 0.364 0.041 
    22 NMBR M73482 0.362 0.035 
    23 RPL26 X69392 0.358 0.035 
    24 PELP1 U88153 0.356 0.042 
    25 MC3R L06155 0.356 0.042 
    26 RPS8 X67247 0.355 0.036 
Resistant     
    1 CAPN1 X04366 −0.496 0.010 
    2 MEL X56741 −0.478 0.010 
    3 PACE X17094 −0.443 0.012 
    4 TIMP2 J05593 −0.433 0.019 
    5 AOP2 D14662 −0.422 0.025 
    6 ZNF174 U31248 −0.402 0.018 
    7 ID3 X69111 −0.393 0.038 
    8 KLF5 D14520 −0.384 0.036 
    9 CALD1 M64110 −0.382 0.031 
    10 LOC54543 AJ011007 −0.368 0.021 
    11 PTPN3 M64572 −0.363 0.038 
    12 ACTB X00351 −0.362 0.025 
    13 LY6E U42376 −0.360 0.037 
    14 ID1 D13889 −0.343 0.044 
RankGeneGenbank IDrP
A. MMC     
Sensitive     
    1 SF1 D26121 0.566 0.001 
    2 CBR3 Ab004854 0.486 0.006 
    3 EMS1 M98343 0.480 0.010 
    4 JUN J04111 0.473 0.015 
    5 SFRS9 U30825 0.448 0.010 
    6 NMBR M73482 0.428 0.012 
    7 RBMX Z23064 0.419 0.012 
    8 SOD1 M13267 0.418 0.024 
    9 NOL1 X55504 0.415 0.025 
    10 PELP1 U88153 0.405 0.019 
    11 ARHA L25080 0.404 0.030 
    12 AARS D32050 0.398 0.018 
    13 NME1 X17620 0.398 0.032 
    14 HNRPA2B1 M29065 0.390 0.044 
    15 NME2 L16785 0.378 0.025 
    16 VAT1 U18009 0.376 0.031 
    17 SERPINB10 U35459 0.372 0.028 
    18 KIAA0436 AB007896 0.353 0.041 
    19 DRPLA D31840 0.350 0.049 
    20 MC3R L06155 0.346 0.049 
Resistant     
    1 SPTBN1 M96803 −0.450 0.013 
    2 PET112L AF026851 −0.425 0.027 
    3 CAPN1 X04366 −0.421 0.032 
    4 MEL X56741 −0.414 0.028 
    5 PACE X17094 −0.380 0.035 
    6 DVL2 AF006012 −0.370 0.034 
    7 LOC54543 AJ011007 −0.366 0.022 
    8 PAPOLA X76770 −0.351 0.033 
    9 RPLP2 M17887 −0.345 0.049 
    10 ARF4L L38490 −0.340 0.042 
B. Vinorelbine     
Sensitive     
    1 ARHA L25080 0.534 0.003 
    2 NME2 L16785 0.521 0.001 
    3 VIL2 X51521 0.463 0.015 
    4 YWHAQ X56468 0.450 0.011 
    5 HK1 M75126 0.449 0.016 
    6 SATB1 M97287 0.439 0.006 
    7 CAMLG U18242 0.439 0.007 
    8 CARS L06845 0.433 0.007 
    9 CCNB1 M25753 0.427 0.013 
    10 U2AF1 M96982 0.424 0.022 
    11 PTMA M26708 0.423 0.018 
    12 MLC1SA M31211 0.397 0.022 
    13 NME1 X17620 0.393 0.035 
    14 SARS X91257 0.386 0.032 
    15 CDC20 U05340 0.385 0.029 
    16 PPP4C X70218 0.385 0.039 
    17 TNFAIP3 M59465 0.384 0.023 
    18 EEF1D Z21507 0.384 0.023 
    19 PFKP D25328 0.365 0.028 
    20 ENTPD2 U91510 0.365 0.037 
    21 CCL5 M21121 0.358 0.035 
    22 ACAT1 D90228 0.352 0.048 
    23 IQGAP1 L33075 0.351 0.042 
    24 PAX5 M96944 0.342 0.038 
    25 NRGN Y09689 0.336 0.042 
    26 K-α-1 K00558 0.328 0.048 
    27 NDUFB7 M33374 0.321 0.049 
Resistant     
    1 HOXB1 X16666 −0.600 0.000 
    2 F10 K03194 −0.514 0.002 
    3 GPX2 X53463 −0.509 0.002 
    4 NR1I2 AF061056 −0.498 0.002 
    5 ANXA4 M19383 −0.481 0.005 
    6 PDLIM1 U90878 −0.465 0.006 
    7 LIPC X07228 −0.464 0.004 
    8 SERPINF2 D00174 −0.447 0.004 
    9 HSD17B1 M36263 −0.443 0.014 
    10 MAN2B1 U60266 −0.440 0.008 
    11 LSS D63807 −0.430 0.014 
    12 PIK3CG X83368 −0.415 0.010 
    13 DBN1 U00802 −0.414 0.017 
    14 NDUFA4 U94586 −0.410 0.038 
    15 BDH M93107 −0.399 0.024 
    16 BCL2L1 Z23115 −0.385 0.039 
    17 EEF1B2 X60656 −0.383 0.030 
    18 F2 V00595 −0.382 0.026 
    19 RARA X06614 −0.369 0.029 
    20 ITGB4 X53587 −0.367 0.042 
    21 IMPA1 X66922 −0.367 0.042 
    22 PACE X17094 −0.367 0.042 
    23 AGA M64073 −0.361 0.042 
    24 MVD U49260 −0.353 0.038 
    25 EHHADH L07077 −0.346 0.039 
    26 TFPI2 D29992 −0.343 0.035 
    27 MARCKS M68956 −0.342 0.045 
    28 FGB J00129 −0.334 0.035 
    29 GPD1 L34041 −0.322 0.049 
C. Paclitaxel     
Sensitive     
    1 ADH6 M68895 0.513 0.002 
    2 RAB28 X94703 0.480 0.007 
    3 U2AF1 M96982 0.441 0.017 
    4 GPC1 X54232 0.440 0.013 
HK1 M75126 0.439 0.020 
    6 CARS L06845 0.436 0.006 
    7 TNFAIP3 M59465 0.433 0.009 
    8 K-α-1 K00558 0.418 0.010 
    9 PFKP D25328 0.416 0.012 
    10 GDI2 D13988 0.411 0.033 
    11 VIL2 X51521 0.410 0.034 
    12 RUNX2 AF001450 0.409 0.038 
    13 NME2 L16785 0.407 0.015 
    14 CDC20 U05340 0.395 0.025 
    15 GNAI2 X04828 0.391 0.033 
    16 ARHA L25080 0.381 0.041 
    17 CNR2 X74328 0.378 0.030 
    18 PPP2R2B M64930 0.376 0.026 
    19 SLC6A8 L31409 0.374 0.046 
    20 DDX9 L13848 0.374 0.042 
    21 ACAT1 D90228 0.369 0.038 
    22 PI3 Z18538 0.329 0.047 
Resistant     
    1 NAP1L1 M86667 −0.530 0.004 
    2 HOXB1 X16666 −0.516 0.004 
    3 PACE X17094 −0.507 0.004 
    4 MAN2B1 U60266 −0.486 0.003 
    5 GPX2 X53463 −0.480 0.004 
    6 DBN1 U00802 −0.469 0.006 
    7 ANXA4 M19383 −0.468 0.007 
    8 SERPINF2 D00174 −0.463 0.003 
    9 AGA M64073 −0.444 0.011 
    10 BCL2L1 Z23115 −0.428 0.021 
    11 LIPC X07228 −0.401 0.015 
    12 BDH M93107 −0.393 0.026 
    13 LSS D63807 −0.384 0.030 
    14 PDLIM1 U90878 −0.372 0.033 
    15 ZNF161 D28118 −0.368 0.038 
    16 UBE2E1 X92963 −0.363 0.032 
    17 TLE1 M99435 −0.360 0.039 
    18 RARA X06614 −0.359 0.034 
    19 PTPRN L18983 −0.357 0.035 
    20 APOE M12529 −0.353 0.048 
    21 F10 K03194 −0.348 0.040 
    22 NR1I2 AF061056 −0.342 0.041 
    23 UBE2L3 X92962 −0.332 0.045 
    24 FGB J00129 −0.313 0.049 
D. SN-38     
Sensitive     
    1 EMS1 M98343 0.573 0.001 
    2 JUN J04111 0.564 0.003 
    3 IL-6 X04602 0.514 0.003 
    4 RPL23 X52839 0.495 0.004 
    5 CDKN3 L25876 0.455 0.017 
    6 RPL3 X73460 0.445 0.011 
    7 TFPI J03225 0.442 0.009 
    8 MRPL3 X06323 0.437 0.009 
    9 HLA-C M11886 0.424 0.014 
    10 AARS D32050 0.419 0.012 
    11 ARHGDIA X69550 0.416 0.031 
    12 NOL1 X55504 0.406 0.029 
    13 SF1 D26121 0.394 0.031 
    14 SOD1 M13267 0.389 0.037 
    15 VEGF M32977 0.384 0.043 
    16 EIF2S1 J02645 0.382 0.034 
    17 CDH5 X79981 0.372 0.030 
    18 FOSL1 X16707 0.371 0.047 
    19 IDS M58342 0.366 0.047 
    20 PMVK L77213 0.364 0.044 
    21 PPP2CB X12656 0.364 0.041 
    22 NMBR M73482 0.362 0.035 
    23 RPL26 X69392 0.358 0.035 
    24 PELP1 U88153 0.356 0.042 
    25 MC3R L06155 0.356 0.042 
    26 RPS8 X67247 0.355 0.036 
Resistant     
    1 CAPN1 X04366 −0.496 0.010 
    2 MEL X56741 −0.478 0.010 
    3 PACE X17094 −0.443 0.012 
    4 TIMP2 J05593 −0.433 0.019 
    5 AOP2 D14662 −0.422 0.025 
    6 ZNF174 U31248 −0.402 0.018 
    7 ID3 X69111 −0.393 0.038 
    8 KLF5 D14520 −0.384 0.036 
    9 CALD1 M64110 −0.382 0.031 
    10 LOC54543 AJ011007 −0.368 0.021 
    11 PTPN3 M64572 −0.363 0.038 
    12 ACTB X00351 −0.362 0.025 
    13 LY6E U42376 −0.360 0.037 
    14 ID1 D13889 −0.343 0.044 

NOTE: Column 2 shows the name of the gene according to HUGO database. Column 4 shows Pearson correlation coefficient between chemosensitivity to drugs and gene expression. “Sensitive” indicates candidate genes sensitive to each drug. “Resistant” indicates genes resistant to each drug.

Table 3.

Genes related to MMC sensitivity in breast, liver, and stomach cancer cell lines

RankGeneGenbank IDrP
A. Breast cancer     
Sensitive     
    1 INHBB M31682 0.972 0.000 
    2 NK4 M59807 0.838 0.018 
    3 HSPA1A M11717 0.751 0.050 
    4 LOC54557 AF075050 0.735 0.024 
    5 CD47 Y00815 0.717 0.045 
Resistant     
    1 RPN2 Y00282 −0.882 0.009 
    2 ATP5O X83218 −0.842 0.017 
    3 CAST D50827 −0.815 0.025 
    4 HPCA D16593 −0.776 0.024 
    5 ZNF9 M28372 −0.774 0.024 
    6 A2LP U70671 −0.772 0.042 
    7 IL-18 D49950 −0.747 0.033 
    8 NRGN Y09689 −0.727 0.041 
B. Liver cancer     
Sensitive     
    1 EB1 U24166 0.872 0.002 
    2 JUN J04111 0.813 0.008 
    3 EIF3S8 U46025 0.772 0.015 
    4 CTSD M11233 0.753 0.012 
    5 SCYA5 M21121 0.741 0.022 
    6 PHB S85655 0.739 0.023 
    7 HSPA1A M11717 0.729 0.026 
    8 SPP1 X13694 0.723 0.018 
    9 TAB7 X93499 0.712 0.021 
    10 ACTN1 X15804 0.692 0.039 
    11 RXRB M84820 0.678 0.045 
    12 PSME2 D45248 0.673 0.047 
    13 HLA-C M11886 0.647 0.043 
    14 RPL19 X63527 0.643 0.033 
Resistant     
    1 MAPK6 X80692 −0.862 0.003 
    2 GCSH M69175 −0.793 0.006 
    3 G22P1 M32865 −0.727 0.017 
    4 USP11 U44839 −0.725 0.027 
    5 ACTB X00351 −0.715 0.020 
    6 YWHAZ M86400 −0.706 0.022 
    7 IL-10 M57627 −0.694 0.018 
    8 RFC4 M87339 −0.677 0.016 
    9 CRLF1 AF059293 −0.644 0.033 
    10 RPS6 M20020 −0.619 0.042 
    11 EMX1 X68879 −0.618 0.043 
    12 TK2 U77088 −0.607 0.047 
C. Stomach cancer     
Sensitive     
    1 TEAD4 U63824 0.803 0.001 
    2 NR2C2 U10990 0.713 0.001 
    3 CSF1 M37435 0.711 0.004 
    4 RAB28 X94703 0.695 0.008 
    5 CBR3 Ab004854 0.683 0.007 
    6 NFYC Z74792 0.639 0.019 
    7 PGF X54936 0.627 0.022 
    8 ERG M21535 0.620 0.005 
    9 MLLT1 L04285 0.613 0.015 
    10 FOS K00650 0.599 0.014 
    11 TNFAIP3 M59465 0.584 0.011 
    12 CNR2 X74328 0.581 0.009 
    13 DRPLA D31840 0.577 0.024 
    14 PSMB5 D29011 0.572 0.026 
    15 SLC6A8 L31409 0.570 0.017 
    16 SERPINB10 U35459 0.570 0.013 
    17 VAT1 U18009 0.570 0.009 
    18 TJP1 L14837 0.562 0.029 
    19 PELP1 U88153 0.545 0.035 
    20 C1QBP L04636 0.545 0.024 
    21 CDK10 L33264 0.543 0.045 
    22 SERPINA6 J02943 0.542 0.025 
    23 ACTB X00351 0.538 0.021 
    24 SFRP4 AF026692 0.538 0.018 
    25 EMX1 X68879 0.535 0.018 
    26 ACTB X00351 0.529 0.024 
    27 RPS9 U14971 0.528 0.043 
    28 AMD1 M21154 0.522 0.038 
    29 RPL26 X69392 0.522 0.038 
    30 HNRPF L28010 0.520 0.047 
    31 PTMS M24398 0.502 0.040 
    32 STK12 AF008552 0.498 0.050 
    33 NR2F6 X12794 0.491 0.046 
    34 GBE1 L07956 0.470 0.049 
Resistant     
    1 PSMD8 D38047 −0.747 0.002 
    2 LAMP2 J04183 −0.677 0.002 
    3 CTSD M11233 −0.651 0.006 
    4 ADORA2B M97759 −0.645 0.005 
    5 ANXA4 M19383 −0.639 0.008 
    6 PTPRK Z70660 −0.638 0.003 
    7 RAD23A D21235 −0.622 0.010 
    8 SDHA D30648 −0.613 0.015 
    9 PET112L AF026851 −0.598 0.024 
    10 DAD1 D15057 −0.593 0.025 
    11 HSPB1 X54079 −0.588 0.013 
    12 PSMA6 X61972 −0.586 0.036 
    13 KDELR1 X55885 −0.584 0.028 
    14 B2M AB021288 −0.581 0.023 
    15 M6PR M16985 −0.579 0.038 
    16 GCLC M90656 −0.576 0.015 
    17 SPTBN1 M96803 −0.557 0.038 
    18 PACE X17094 −0.547 0.019 
    19 RPL24 M94314 −0.539 0.017 
    20 SPINT2 U78095 −0.538 0.039 
    21 STX4A U07158 −0.534 0.027 
    22 SIAT8B U33551 −0.532 0.028 
    23 CTSK U13665 −0.529 0.029 
    24 DCI L24774 −0.525 0.044 
    25 MEL X56741 −0.525 0.045 
    26 PITPNB D30037 −0.523 0.038 
    27 YY1 M76541 −0.512 0.043 
    28 RAB1 M28209 −0.495 0.037 
    29 UBE2L6 AF031141 −0.492 0.045 
    30 PSMB7 D38048 −0.484 0.049 
RankGeneGenbank IDrP
A. Breast cancer     
Sensitive     
    1 INHBB M31682 0.972 0.000 
    2 NK4 M59807 0.838 0.018 
    3 HSPA1A M11717 0.751 0.050 
    4 LOC54557 AF075050 0.735 0.024 
    5 CD47 Y00815 0.717 0.045 
Resistant     
    1 RPN2 Y00282 −0.882 0.009 
    2 ATP5O X83218 −0.842 0.017 
    3 CAST D50827 −0.815 0.025 
    4 HPCA D16593 −0.776 0.024 
    5 ZNF9 M28372 −0.774 0.024 
    6 A2LP U70671 −0.772 0.042 
    7 IL-18 D49950 −0.747 0.033 
    8 NRGN Y09689 −0.727 0.041 
B. Liver cancer     
Sensitive     
    1 EB1 U24166 0.872 0.002 
    2 JUN J04111 0.813 0.008 
    3 EIF3S8 U46025 0.772 0.015 
    4 CTSD M11233 0.753 0.012 
    5 SCYA5 M21121 0.741 0.022 
    6 PHB S85655 0.739 0.023 
    7 HSPA1A M11717 0.729 0.026 
    8 SPP1 X13694 0.723 0.018 
    9 TAB7 X93499 0.712 0.021 
    10 ACTN1 X15804 0.692 0.039 
    11 RXRB M84820 0.678 0.045 
    12 PSME2 D45248 0.673 0.047 
    13 HLA-C M11886 0.647 0.043 
    14 RPL19 X63527 0.643 0.033 
Resistant     
    1 MAPK6 X80692 −0.862 0.003 
    2 GCSH M69175 −0.793 0.006 
    3 G22P1 M32865 −0.727 0.017 
    4 USP11 U44839 −0.725 0.027 
    5 ACTB X00351 −0.715 0.020 
    6 YWHAZ M86400 −0.706 0.022 
    7 IL-10 M57627 −0.694 0.018 
    8 RFC4 M87339 −0.677 0.016 
    9 CRLF1 AF059293 −0.644 0.033 
    10 RPS6 M20020 −0.619 0.042 
    11 EMX1 X68879 −0.618 0.043 
    12 TK2 U77088 −0.607 0.047 
C. Stomach cancer     
Sensitive     
    1 TEAD4 U63824 0.803 0.001 
    2 NR2C2 U10990 0.713 0.001 
    3 CSF1 M37435 0.711 0.004 
    4 RAB28 X94703 0.695 0.008 
    5 CBR3 Ab004854 0.683 0.007 
    6 NFYC Z74792 0.639 0.019 
    7 PGF X54936 0.627 0.022 
    8 ERG M21535 0.620 0.005 
    9 MLLT1 L04285 0.613 0.015 
    10 FOS K00650 0.599 0.014 
    11 TNFAIP3 M59465 0.584 0.011 
    12 CNR2 X74328 0.581 0.009 
    13 DRPLA D31840 0.577 0.024 
    14 PSMB5 D29011 0.572 0.026 
    15 SLC6A8 L31409 0.570 0.017 
    16 SERPINB10 U35459 0.570 0.013 
    17 VAT1 U18009 0.570 0.009 
    18 TJP1 L14837 0.562 0.029 
    19 PELP1 U88153 0.545 0.035 
    20 C1QBP L04636 0.545 0.024 
    21 CDK10 L33264 0.543 0.045 
    22 SERPINA6 J02943 0.542 0.025 
    23 ACTB X00351 0.538 0.021 
    24 SFRP4 AF026692 0.538 0.018 
    25 EMX1 X68879 0.535 0.018 
    26 ACTB X00351 0.529 0.024 
    27 RPS9 U14971 0.528 0.043 
    28 AMD1 M21154 0.522 0.038 
    29 RPL26 X69392 0.522 0.038 
    30 HNRPF L28010 0.520 0.047 
    31 PTMS M24398 0.502 0.040 
    32 STK12 AF008552 0.498 0.050 
    33 NR2F6 X12794 0.491 0.046 
    34 GBE1 L07956 0.470 0.049 
Resistant     
    1 PSMD8 D38047 −0.747 0.002 
    2 LAMP2 J04183 −0.677 0.002 
    3 CTSD M11233 −0.651 0.006 
    4 ADORA2B M97759 −0.645 0.005 
    5 ANXA4 M19383 −0.639 0.008 
    6 PTPRK Z70660 −0.638 0.003 
    7 RAD23A D21235 −0.622 0.010 
    8 SDHA D30648 −0.613 0.015 
    9 PET112L AF026851 −0.598 0.024 
    10 DAD1 D15057 −0.593 0.025 
    11 HSPB1 X54079 −0.588 0.013 
    12 PSMA6 X61972 −0.586 0.036 
    13 KDELR1 X55885 −0.584 0.028 
    14 B2M AB021288 −0.581 0.023 
    15 M6PR M16985 −0.579 0.038 
    16 GCLC M90656 −0.576 0.015 
    17 SPTBN1 M96803 −0.557 0.038 
    18 PACE X17094 −0.547 0.019 
    19 RPL24 M94314 −0.539 0.017 
    20 SPINT2 U78095 −0.538 0.039 
    21 STX4A U07158 −0.534 0.027 
    22 SIAT8B U33551 −0.532 0.028 
    23 CTSK U13665 −0.529 0.029 
    24 DCI L24774 −0.525 0.044 
    25 MEL X56741 −0.525 0.045 
    26 PITPNB D30037 −0.523 0.038 
    27 YY1 M76541 −0.512 0.043 
    28 RAB1 M28209 −0.495 0.037 
    29 UBE2L6 AF031141 −0.492 0.045 
    30 PSMB7 D38048 −0.484 0.049 

NOTE: Column 2 shows the name of the gene according to HUGO database. Column 4 shows Pearson correlation coefficient between chemosensitivity to drugs and gene expression. “Sensitive” indicates candidate genes sensitive to each drug. “Resistant” indicates genes resistant to each drug.

Identification of Genes That Change Cellular Chemosensitivity

These genes described above may include genes that directly determine chemosensitivity. To identify such genes, we established a screening system in which we could detect any change in the anticancer drug sensitivity by monitoring cell growth inhibition. [3H]thymidine incorporation was used as a variable to measure cell growth. To detect small changes in sensitivity, a higher transfection efficiency was required. Therefore, the human fibrosarcoma cell line, HT1080, which reportedly showed high transfection efficiency, was selected for the subsequent experiments. Transfection efficiency of HT1080 cells was >90% as evaluated by transfection of a plasmid expressing the enhanced green fluorescent protein (data not shown). To validate this screening system, we examined the effect of NQO1 gene, coding DT-diaphorase that increases cellular sensitivity to MMC (12). As shown in Fig. 3B, cells transfected with NQO1 significantly enhanced growth inhibition by MMC compared with the mock-transfected and LacZ-transfected cells. We confirmed the cellular expression of the NQO1 gene product by immunoblot (Fig. 3C). Thus, this screening system can be used to detect changes in chemosensitivity in HT1080 cells. Using this screening system, we examined whether the 19 genes, which were extracted in Tables 2 and 3, altered sensitivity to drug. Notably, the HSPA1A gene coding 70-kDa heat shock protein, whose expression was correlated with MMC sensitivity in the breast and liver cancer cell lines, significantly enhanced the MMC sensitivity in HSPA1A-transfected HT1080 cells (Fig. 3B). Similarly, the JUN gene encoding c-JUN, whose expression was correlated with MMC sensitivity, also enhanced the MMC sensitivity in JUN-transfected HT1080 cells (Fig. 3B). The expression of myc-tagged LacZ, 70-kDa heat shock protein, and JUN in the transfected cells was confirmed by immunoblotting with anti-myc antibody (Fig. 3C). Transfection with 17 other genes did not alter the MMC sensitivity. For example, transfection with the IL-18 gene did not affect MMC sensitivity (Fig. 3B).

Figure 3.

Relationships between MMC sensitivity and expression of HSPA1A in breast cancer cell lines (A, left) or JUN in 42 cell lines (A,right). Each symbol indicates one cell line. X axis, MMC sensitivity; Y axis, expression of HSPA1A or JUN. Pearson correlation coefficients between MMC sensitivity and expression of HSPA1A and JUN were 0.75 (P = 0.05) and 0.473 (P = 0.015), respectively. B, growth inhibition curves by MMC in mock (▪), LacZ (⧫), NQO1 (×), HSPA1A (▴), JUN (▵), or IL-18 (□) transfected HT1080 cells. This growth inhibition by MMC was enhanced in HT1080 cells transfected with NQO1, HSPA1A, and JUN. *, P < 0.002; **, P < 0.0001, t test against mock-transfected cells. C, expressions of genes were certified by immunoblotting with anti-myc antibody: myc-tagged LacZ (lane 2), NQO1 (lane 3), 70-kDa heat shock protein (HSP70; lane 4), and JUN (lane 5).

Figure 3.

Relationships between MMC sensitivity and expression of HSPA1A in breast cancer cell lines (A, left) or JUN in 42 cell lines (A,right). Each symbol indicates one cell line. X axis, MMC sensitivity; Y axis, expression of HSPA1A or JUN. Pearson correlation coefficients between MMC sensitivity and expression of HSPA1A and JUN were 0.75 (P = 0.05) and 0.473 (P = 0.015), respectively. B, growth inhibition curves by MMC in mock (▪), LacZ (⧫), NQO1 (×), HSPA1A (▴), JUN (▵), or IL-18 (□) transfected HT1080 cells. This growth inhibition by MMC was enhanced in HT1080 cells transfected with NQO1, HSPA1A, and JUN. *, P < 0.002; **, P < 0.0001, t test against mock-transfected cells. C, expressions of genes were certified by immunoblotting with anti-myc antibody: myc-tagged LacZ (lane 2), NQO1 (lane 3), 70-kDa heat shock protein (HSP70; lane 4), and JUN (lane 5).

Close modal

The assessment system for determining pharmacologic properties of chemicals by a panel of cancer cell lines was first developed in the National Cancer Institute (33–35). We established a similar assessment system (JFCR-39; ref. 32) and showed that drugs with similar modes of actions were classified into the same cluster by hierarchical clustering (19). In this study, we constructed a new panel of 45 human cancer cell lines (JFCR-45), comprising cancer cell lines derived from tumors from three different organ types: breast, liver, and stomach. In particular, the inclusion of cell lines derived from gastric and hepatic cancers is a major point of novelty. JFCR-45 can be used for analyzing both organ-specific differences in chemosensitivity and intraorgan heterogeneity of chemosensitivity. We examined 53 anticancer drugs for their activity against JFCR-45 and observed differential activity across the whole panel as well as within a single organ type (e.g., breast, liver, or stomach). Furthermore, as shown in Fig. 1, using JFCR-45, drugs with a similar mode of action (such as a tubulin binder or topo I inhibitor) were classified into the same cluster, which were the same as the clusters established for NCI-60 (35) and JFCR-39 (19). These results suggest that the cell line panel-based assessment system is generally effective for classifying anticancer drugs with the same modes of action into the same set of clusters.

In this study, we investigated the gene expression profiles of 42 cell lines of JFCR-45 using cDNA array consisting of 3,537 genes. Hierarchical clustering analysis of these gene expression profiles classified organ-specific cell lines mostly into the same cluster, suggesting that these cell lines maintained the genetic characteristics of the parent organ as far as the gene expression profiles were concerned.

We did a Pearson correlation analysis of the gene expression database and the drug sensitivity database. Consequently, many genes whose expressions were correlated with respect to the sensitivity of each drug were identified. For example, DNA alkylating agents and nucleic acid–related genes, including SF1 encoding ZFM1, c-JUN oncogene, and SFRS9 were extracted as the genes sensitive to MMC. The genes that were sensitive to paclitaxel included tubulin binder and cytoskeleton-related genes, such as VIL2 encoding ezrin and ACTB encoding β-actin.

These results suggest that the extracted genes are the predictive markers of drug efficacy. We further applied Pearson correlation analysis to each type (i.e., breast, liver, or stomach cancer) of cell lines. There were two advantages in this type of analysis: one is that we could compare the cell lines having the same organ background and another is that organ-specific genes, which worked as the sensitive or resistant factors, could be extracted. For example, for MMC, several genes (such as INHBB, NK4, and HSPA1A) were newly extracted as candidate genes sensitive to MMC from the breast cancer cell lines. Surprisingly, compared with the breast and liver cancer cells, many new candidate genes were extracted from the stomach cancer cell lines. These extracted genes were considered as the candidates for organ-specific predictive markers of drug efficacy.

We hypothesized that some of the candidate sensitivity genes described above might causally affect the chemosensitivity of cancer cell lines. To validate this possibility, we selected 19 genes, including HSPA1A, JUN, and IL-18, and examined whether the expression of these candidate genes would affect the cellular sensitivity to anticancer drugs. Overexpression of 2 of the 19 genes, HSPA1A encoding 70-kDa heat shock protein and JUN encoding c-JUN, indeed enhanced cellular sensitivity to MMC in HT1080 cells (Fig. 3), suggesting that they function to mediate MMC sensitivity. This was an unexpected finding, because a direct relationship between these two genes and MMC sensitivity has not been reported previously, although a relationship between heat shock protein and cancer has been suggested previously (36, 37). How these two genes potentiate MMC sensitivity remains to be clarified. In this validation, we used the HT1080 cell line instead of those in JFCR-45 because of its high transfection efficiency. As the alteration of chemosensitivity following the overexpression of any particular gene may depend highly on the genotypic/phenotypic background of the transfected HT1080 cells, further validation using cell lines within JFCR-45 will be required. In addition to the overexpression experiments, validation by silencing chemosensitivity-related genes using small interfering RNA will be required.

Pioneering attempts to discover new leads and targets and to investigate new aspects of the molecular pharmacology of anticancer drugs by mining the NCI-60 database have been done (31, 33–35). Recently, Szakacs et al. (38) have identified interesting compounds whose activity is potentiated by the MDR1 multidrug transporter. Our previous studies using JFCR-39 (19, 20, 31) and the present study using JFCR-45 also indicate that a comprehensive analysis of chemosensitivity and gene expression data followed by experimental validation leads to the identification of genes that determine drug sensitivity.

In conclusion, we established a sensitivity database for JFCR-45, which focused on organ origin, to 53 anticancer drugs. Using JFCR-45, anticancer drugs were classified according to their modes of action. Moreover, we established a database of the gene expression profiles in 42 cell lines of JFCR-45. Using these two databases, we have identified several genes that may predict chemosensitivity of cancer. Among these candidate genes, we identified two genes, HSPA1A and JUN, which determined sensitivity to MMC. Thus, this approach is useful not only to discover predictive markers for the efficacy of anticancer drugs but also to discover genes that determine chemosensitivity.

Grant support: Ministry of Education, Culture, Sports, Science and Technology of Japan Aid for Scientific Research on Priority Areas; Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research (B) and Exploratory Research; and research grant from the Princess Takamatsu Cancer Research Fund.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

We thank Yumiko Mukai, Yumiko Nishimura, Mariko Seki, and Fujiko Ohashi for the determination of chemosensitivity and Dr. Munetika Enjoji (Department of Internal Medicine, National Kyushu Cancer Center, Fukuoka, Japan) for providing the RBE and SSP-25 cell lines.

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