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Evaluation of Action Mechanisms of Toxic Chemicals Using JFCR39, a Panel of Human Cancer Cell Lines

Division of Molecular Pharmacology, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Koto-ku, Tokyo, Japan (N.N., T.N., K.Y., T.Y.); Division of Cellular and Molecular Toxicology, Biological Safety Research Center, National Institute of Health Sciences, Setagaya-ku, Tokyo, Japan (N.N., J.K.); and Second Department of Surgery, Tokai University School of Medicine, Boseidai, Isehara-City, Kanagawa, Japan (T.N., S.S., H.M.)

Received June 6, 2007; accepted August 16, 2007

	Abstract

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We previously established a panel of human cancer cell lines, JFCR39, coupled to an anticancer drug activity database; this panel is comparable with the NCI60 panel developed by the National Cancer Institute. The JFCR39 system can be used to predict the molecular targets or evaluate the action mechanisms of the test compounds by comparing their cell growth inhibition profiles (i.e., fingerprints) with those of the standard anticancer drugs using the COMPARE program. In this study, we used this drug activity database-coupled JFCR39 system to evaluate the action mechanisms of various chemical compounds, including toxic chemicals, agricultural chemicals, drugs, and synthetic intermediates. Fingerprints of 130 chemicals were determined and stored in the database. Sixty-nine of 130 chemicals (60%) satisfied our criteria for the further analysis and were classified by cluster analysis of the fingerprints of these chemicals and several standard anticancer drugs into the following three clusters: 1) anticancer drugs, 2) chemicals that shared similar action mechanisms (for example, ouabain and digoxin), and 3) chemicals whose action mechanisms were unknown. These results suggested that chemicals belonging to a cluster (i.e., a cluster of toxic chemicals, a cluster of anticancer drugs, etc.) shared similar action mechanism. In summary, the JFCR39 system can classify chemicals based on their fingerprints, even when their action mechanisms are unknown, and it is highly probable that the chemicals within a cluster share common action mechanisms.

A number of screening methods are currently available for discovering new anticancer drugs. One very powerful and unique approach using multiple cancer cell lines was developed at NCI (Paull et al., 1989; Weinstein et al., 1992, 1997) and in our laboratory (Yamori et al., 1999; Dan et al., 2002, 2003; Yamori, 2003; Nakatsu et al., 2005; Akashi and Yamori, 2007; Akashi et al., 2007; Nakamura et al., 2007). This bioinformatics-based approach enables mechanism-oriented evaluation of anticancer drugs. For example, we can evaluate the cell toxicity in vitro by determining the 50% growth inhibition (GI50), total growth inhibition, and 50% lethal concentration across a panel of 39 human cancer cell lines (JFCR39). We can also predict the molecular targets or evaluate the action mechanisms of the test compounds by comparing the cell growth inhibition profiles (termed "fingerprints") across the panel for these compounds with those of the standard anticancer drugs using the COMPARE algorithm (Yamori et al., 1999). We have used this system successfully and demonstrated that the molecular targets of the novel chemicals MS-274, FJ5002, and ZSTK474 were topoisomerases I and II (Yamori et al., 1999), telomerase (Naasani et al., 1999), and phosphatidylinositol 3-kinase (Yaguchi et al., 2006), respectively. Several other interesting studies, based on a panel of cancer cells, classified anticancer drugs according to their action mechanisms or molecular targets by cluster analysis of their GI50 values (Weinstein et al., 1992, 1997; Dan et al., 2002). Correlation analysis has also been used to explore the genes associated with the sensitivity of the cells in the panel to anticancer drugs (Scherf et al., 2000; Okutsu et al., 2002; Zembutsu et al., 2002; Nakatsu et al., 2005).

View larger version (32K): [in this window] [in a new window]   Fig. 1. Dose response curves of digoxin against growth of JFCR-39 cells. The x-axis represents concentration of digoxin and the y-axis represents percentage growth. The GI50 represents the concentration required to inhibit cell growth by 50% compared with untreated controls.

View larger version (29K): [in this window] [in a new window]   Fig. 2. Fingerprints of digoxin, ouabain, SV-NN, and SV-NNK. Fingerprint shows the differential growth inhibition pattern of the cells in the JFCR-39 panel against the test chemical. The X-axis represents relative value of GI50; (–1) x (log GI50 – MG-MID); MG-MID is the mean value of the log GI50. Zero means the mean GI50 and one means the GI50 value is 10-fold more sensitive than the mean GI50. Exp-ID and JCI numbers are the ID for the experiment and ID for the chemical, respectively, in our database.

	Materials and Methods

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Determination of Cell Growth Inhibition Profiles. Growth inhibition experiments were performed to assess the sensitivity of the cells to various chemicals as described before (Yamori et al., 1999; Dan et al., 2002). Growth inhibition was measured by determining the changes in the amounts of total cellular protein after 48 h of chemical treatment using a sulforhodamine B assay. For each chemical, the growth assay was performed using a total of five different concentrations of the chemical (for example, 10^–4,10^–5, 10^–6,10^–7, and 10^–8 M) and one negative control. All assays were performed in duplicate. This GI50 calculation method is well established and reliable through anticancer drug screen using NCI60 as well as JFCR39 (Paull et al., 1989; Yamori et al., 1999; Yamori, 2003). At each test concentration, the percentage growth was calculated using the following seven absorbance measurements: growth at time 0 (T0), growth of the control cells (C), and test growth in the presence of five different concentrations (T) of a drug. The percentage growth inhibition was calculated as: % growth = 100 x [(T – T0)/(C – T0)] when T T0, and % growth = 100 x [(T – T0)/T] when T < T0. The GI50 values, which represent 50% growth inhibition concentration, were calculated as 100 x [(T – T0)/(C – T0)] = 50. When the GI50 of a chemical could not be calculated, the highest used concentration was assigned as its GI50 value. Absolute values of GI50 were then log transformed for further analysis. We certified the accuracy of measured GI50 data by using reference control chemicals, such as mitomycin-C, paclitaxel, and SN-38, in every experiment and by checking the dose response curves.

Chemicals. Spironolactone, para-aminoazobenzene, para-cresidine, neostigmine bromide, para-dichlorobenzene, phenytoin, ortho-toluidine, imipramine, cobalt chloride, atrazine, propylthiouracil, (D,L)-thalidomide, carbon tetrachloride, hydroquinone, monocrotaline, vinyl chloride, tributyl-tin chloride, valproic acid, benzene, acrylamide, pentachlorophenol, aniline, 1,3-diphenylguanidine, polypropylene glycol, 10,10'-oxy-bis(phenoxyarsine), testosterone propionate, carbaryl, acephate, bisphenol A, 17--estradiol, diethylstilbestrol, and -bungarotoxin were purchased from Wako (Tokyo, Japan). Snake venoms from Agkistrodon halys blomhoffii, Trimeresurus flavoviridis, Crotalus atrox, Naja nigricollis, and Naja naja kaouthia were purchased from Latoxan (Valence, France). 2-Aminomethylpyridine, 1H-1,2,4-triazole, 1H-1,2,3-triazole, 3,4,4'-trichlorocarbanilide, edifenphos, dichlorvos, O-ethyl O-4-nitrophenyl phenylphosphonothioate, 2,4-dinitrophenol, N-methylaniline, 1,2-dichloro-3-nitrobenzene, 4-ethylnitrobenzene, 2-vinylpyridine, 3-amino-1H-1,2,4-triazole, N-ethyl-N-nitrosourea, 5-aza-2'-deoxycytidine, ethynyl estradiol, 3-methylcholanthrene, phenobarbital, acetaminophen, isoniazid, capsaicin, N-deacetyl-N-methylcolchicine (Colcemid), 2.4-dinitrochlorobenzene, and dexamethasone were from Sigma Chemicals (St. Louis, MO). Methoprene acid, methoprene, all-trans retinoic acid, and 9-cis retinoic acid were from BIOMOL International L.P. (Plymouth Meeting, PA). Levothyroxine was from MP Biomedicals (Irvine, CA). 3-Iodo-2-propynyl butylcarbamate was from Olin Japan Inc. (Tokyo, Japan), p-chlorophenyl-3-iodopropargylformal was from Nagase ChemteX (Osaka, Japan), and 2,3,3,3–2',3',3',3'-octachlorodipropylether was from Sankyo Chemical Industries, Ltd. (Tokyo, Japan). 1,2-Benzisothiazolin-3-one was from Riverson (Osaka, Japan), zinc butylxanthate was from Ouchishinko Chemical Industrial Co., Ltd. (Tokyo, Japan), and 4-amino-2,6-dichlorophenol was from Tokyo Kasei Kogyo Co. Ltd. (Tokyo, Japan).

Hierarchical Clustering. Hierarchical clustering analysis was carried out using the average linkage method and the "GeneSpring" software (Silicon Genetics, Inc., Redwood, CA). Pearson correlation coefficients were used to determine the degree of similarity.

	Results

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Sensitivity of JFCR39 to Chemicals. Sensitivity of the JFCR39 panel of cells to 130 chemicals was determined as described under Materials and Methods. Table 1 summarizes abbreviations, applications, targets, and known mechanisms of 130 chemicals and 21 anticancer drugs. Approximately 15% of the chemicals were assessed twice or more. Approximately 40% of the chemicals tested had little effect on the growth of cells in the JFCR39 panel. However, the rest of the chemicals significantly inhibited the cell growth across the JFCR39 panel. For example, Fig. 1 shows the dose response curves of the cells in the JFCR39 panel against digoxin. The concentration at which the cell growth is inhibited by 50% represents GI50. Figure 2 shows the fingerprints of four chemicals [digoxin, ouabain, snake venom from N. nigricollis (SV-NN), and snake venom from N. naja kaouthia (SV-NNK)], which differentially inhibited the growth of cells in the JFCR39 panel; these fingerprints were drawn based on a calculation using a set of GI50s and clearly represented the GI50 pattern. These results were highly reproducible in that the Pearson correlation coefficient of the duplicate experiments for digoxin was 0.839 (p < 0.001) and that for ouabain was 0.864 (p < 0.001). It is noteworthy that, digoxin and ouabain, both of which are cardiac glycosides and inhibit Na-K ATPase, showed similar fingerprints. The fingerprints of SV-NNK and SV-NN, which belong to the elapidae, known as cobras, were also similar, but were different from the fingerprints of digoxin and ouabain. Table 2 summarizes only a portion of the GI50 values from 160 experiments involving 130 chemicals and 42 experiments involving 21 anticancer drugs. GI50 values from all experiments are described in the Supplemental Data (Table S1). All these data were stored in a chemosensitivity database and used for further analysis.

Classification of the Chemicals by Hierarchical Clustering. Sixty-nine chemicals were selected for further analysis based on the following criteria: 1) GI50 values for the test chemical can be determined for at least 10 cell lines in the JFCR39 panel, and 2) the range of log GI50 for the test chemical is more than 0.6, suggesting differential growth inhibition. We analyzed the GI50 values of these 69 chemicals and 20 anticancer drugs by hierarchical clustering analysis (Fig. 3). We found approximately 12 clusters (threshold: r = 0, Fig. 3, clusters A–L), which were further divided into 49 subclusters (threshold: r = 0.408, Fig. 3, clusters A1–L6).

View larger version (52K): [in this window] [in a new window]   Fig. 3. Hierarchical clustering of 69 test chemicals and 20 anticancer drugs based on their GI50 values. Hierarchical clustering method was an "average linkage method" using the Pearson correlation as distance. We classified the chemicals into two kinds of clusters; their threshold values were r = 0 and r = 0.408 (p < 0.01), respectively. Gradient color indicates relative level (log transformed) of GI50. Red, more sensitive than the mean GI50 (2.0); yellow, mean GI50 (0.0); and green, less sensitive than the mean GI50 (–2.0). On the color scale, red represents the GI50 value that is 100-fold higher than the mean GI50.

	Discussion

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The JFCR39 system coupled to a drug activity database is a good model for investigating the diversity of chemosensitivity in cancer cells. We have previously established panels of human cancer cell lines [JFCR39 (Yamori, 2003) and JFCR45 (Nakatsu et al., 2005)]. We used these panels of cells to demonstrate that they provide powerful means to predict the action mechanisms of drugs, and also used them to identify new target compounds. In this manuscript, we used the JFCR39 system to evaluate various chemicals (such as toxic chemicals, agricultural chemicals, and synthetic intermediates), which are not anticancer drugs, and classified them according to their molecular target or action mechanism. As a result, these chemicals were classified into a number of clusters. Our results also suggested that each cluster consisted of chemicals sharing a common action mechanism.

We determined the growth inhibition of cells in the JFCR39 panel by 130 chemicals and calculated their GI50 values. Some of the chemicals were assessed twice or more to confirm the reproducibility of the assay. We had to exclude 61 chemicals from further analysis because they did not inhibit the cells in the JFCR39 panel significantly. The rest of the chemicals (69 of 130, 60%) met our selection criteria and were evaluated by cluster analysis.

First, we found that the chemicals tested in duplicate formed tight clusters, showing high reproducibility. Next, we investigated the difference between these 69 test chemicals and the anticancer drugs. Sixty-nine chemicals, which are not anticancer drugs, formed several clusters, which were different from the anticancer drug clusters. These results suggest that the action mechanisms of these chemicals are different from the action mechanisms of the anticancer drugs. However, we found that cisplatin did not belong to the cluster A, which consisted of DNA-targeting anticancer drugs. We do not understand the reason at present. However, there is a possibility that cisplatin has other action mechanisms, which may have made the fingerprint of cisplatin different from those of other DNA-targeting drugs. Indeed, cisplatin is known to form DNA-protein cross-links (Zwelling et al., 1979; Chválová et al., 2007).

Our analysis also identified several interesting clusters. For example, the cluster F3 consisted of cardiac glycosides digoxin and ouabain, both of which inhibit Na-K ATPase (Reuter et al., 2002). The cluster D1 consisted of 9-cis retinoic acid, 13-cis retinoic acid, and 4-[E-2-(5,6,7,8-tetrahydro-5,5,8,8-tetra-methyl-2-naphthalenyl)-1-propenyl]benzoic acid, which are RAR agonists. These results suggest that chemicals other than the anticancer drugs also form clusters when they share the same action mechanisms. It is noteworthy that SV-NNK and SV-NN, from snakes that belonged to the elapidae family, formed one cluster (cluster D2). In contrast, the snake venoms from the C. atrox and T. flavoviridis, which belonged to the viperidae family, formed a cluster (cluster D3) different from the elapidae cluster. These results are reasonable because snake venoms from different snake families are known to differ not only in composition but also in levels of toxicity and mechanisms of action.

The agricultural chemicals paraquat, ziram, and thiram were also classified into a single cluster (cluster F1). Among these agricultural chemicals, the action mechanism of ziram is not known. However, both paraquat and thiram are known to induce oxidative stress (Cereser et al., 2001; Suntres, 2002). Therefore, based on our observations, we could suggest that ziram also acted by inducing oxidative stress. The agricultural chemicals methoprene (insect growth regulator) and carbaryl (chorine esterase inhibitor) formed cluster L3, although their common mechanism is unknown. Cluster D4 and D5 consist of the antibacterial agents or fungicides. 3-Iodo-2-propynyl-butylcarbamate and p-chlorophenyl-3'-iodopropargylformal, belonging to cluster D4, are the iodotype antibacterial agents.

Thus, cluster analysis of GI50 values of various chemicals, determined using the JFCR39 cell panel, suggests that the JFCR39 system could, at least in part, allow classification of chemical compounds on the basis of their action mechanisms. Our analysis also suggests that the chemicals belonging the same cluster share a common action mechanism. We are going to develop a larger library of reference chemicals with known action mechanisms (i.e., various inhibitors of biological pathways), and expand our database by integrating their GI50 measurements, which will make the cluster analysis as well as the COMPARE analysis more informative for predicting the mechanism of test chemicals.

In conclusion, to evaluate the potential of the JFCR39 system in predicting the action mechanisms of toxic chemicals, we investigated the fingerprints of 130 different types of chemical compounds including toxic chemicals, pesticides, drugs, and synthetic intermediates. Using the hierarchical clustering analysis, we classified 69 chemicals, at least in part, based on their action mechanisms. Thus, this approach using the JFCR39 cell panel is useful not only in predicting the action mechanisms of toxic chemicals but also in evaluating their toxicity.

	Acknowledgements

 
We thank Yumiko Mukai, Yumiko Nishimura, and Mariko Seki for determination of chemosensitivity and Satoshi Kitajima for help with chemical information.

	Footnotes

 
This work was supported in part by Grant-in-Aid 17390032 for Scientific Research (B) from Japan Society for the Promotion of Science (to T.Y.); Ministry of Health, Labor, and Welfare Grants-in-Aid H15-kagaku-002, H16-kagaku-003 (to T.Y. and J.K.); Grant-in-Aid 18015049 of the Priority Area "Cancer" from the Ministry of Education, Culture, Sports, Science and Technology of Japan (to T.Y.); and grant 05-13 from National Institute of Biomedical Innovation Japan (to T.Y.)

N. N. and T. N. equally contributed to this study.

Article, publication date, and citation information can be found at http://molpharm.aspetjournals.org.

doi:10.1124/mol.107.038836.

ABBREVIATIONS: GI50, 50% growth inhibition concentration; GI50, 50% growth inhibition; SN-38, 7-ethyl-10-hydroxycamptothecin; SV-NN, snake venom from N. nigricollis; SV-NNK; snake venom from N. naja kaouthia.

The online version of this article (available at http://molpharm.aspetjournals.org) contains supplemental material.

Address correspondence to: Takao Yamori, Division of Molecular Pharmacology, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, 3-10-6, Ariake, Koto-ku, Tokyo 135-8550, Japan. E-mail: yamori{at}jfcr.or.jp, 07a$sl

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This Article Abstract Full Text (PDF) Data Supplement All Versions of this Article: mol.107.038836v1 72/5/1171    most recent Submit a response Alert me when this article is cited Alert me when eLetters are posted Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Nakatsu, N. Articles by Yamori, T. Search for Related Content PubMed PubMed Citation Articles by Nakatsu, N. Articles by Yamori, T.