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).

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.

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.

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).

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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