An Integrated Database of Chemosensitivity to 55 Anticancer Drugs and Gene Expression Profiles of 39 Human Cancer Cell Lines1

[CANCER RESEARCH 62, 1139–1147, February 15, 2002] An Integrated Database of Chemosensitivity to 55 Anticancer Drugs and Gene Expression Profiles of 39 Human Cancer Cell Lines1

Shingo Dan, Tatsuhiko Tsunoda, Osamu Kitahara, Rempei Yanagawa, Hitoshi Zembutsu, Toyomasa Katagiri, Kanami Yamazaki, Yusuke Nakamura, and Takao Yamori2 Division of Molecular Pharmacology, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Toshima-ku, Tokyo 170-8455 [S. D., K. Y., T. Y.]; Laboratory of Molecular Medicine, Human Genome Center, Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo 108-8639 [O. K., R. Y., H. Z., T. K., Y. N.]; and SNP Research Center, RIKEN (Institute of Physical and Chemical Research), Minato-ku, Tokyo 108-8639 [T. T.], Japan

ABSTRACT Taking advantage of a panel of 39 well-characterized human cancer cell lines (2) and a cDNA microarray system consisting of 9216 genes To explore genes that determine the sensitivity of cancer cells to anticancer (3), we attempted to reveal genes associated with cancer cell chemo- drugs, we investigated using cDNA microarrays the expression of 9216 genes sensitivity. Here, we report the construction of an integrated database in 39 human cancer cell lines pharmacologically characterized on treatment with various anticancer drugs. A bioinformatical approach was then ex- of gene expression profiles and drug sensitivity patterns for 39 human ploited to identify genes related to anticancer drug sensitivity. An integrated cancer cell lines and the identification of gene sets that are likely to be database of gene expression and drug sensitivity profiles was constructed and involved in chemosensitivity. used to identify genes with expression patterns that showed significant correlation to patterns of drug responsiveness. As a result, sets of genes were extracted for each of the 55 anticancer drugs examined. Whereas some genes MATERIALS AND METHODS commonly correlated with various classes of anticancer drugs, other genes Cell Lines and Cell Culture. The human cancer cell line panel has been correlated only with specific drugs with similar mechanisms of action. This described previously (2) and consists of the following 39 human cancer cell latter group of genes may reflect the efficacy of each class of drugs. Therefore, lines: lung cancer, NCI3-H23, NCI-H226, NCI-H522, NCI-H460, A549, the integrated database approach of gene expression and chemosensitivity DMS273, and DMS114; colorectal cancer, HCC-2998, KM-12, HT-29, HCT- profiles may be useful in the development of systems to predict drug efficacies 15, and HCT-116; gastric cancer, MKN-1, MKN-7, MKN-28, MKN-45, of cancer cells by examining the expression levels of particular genes. MKN-74, and St-4; ovarian cancer, OVCAR-3, OVCAR-4, OVCAR-5, OVCAR-8, and SK-OV-3; breast cancer, BSY-1, HBC-4, HBC-5, MDA-MB- INTRODUCTION 231, and MCF-7; renal cancer, RXF-631L and ACHN; melanoma, LOX- IMVI; glioma, U251, SF-295, SF-539, SF-268, SNB-75, and SNB-78; and Despite enormous efforts spent in the development of cancer chemo- prostate cancer, DU-145 and PC-3. All cell lines were cultured in RPMI 1640 therapies, often these therapies are effective only in a relatively small supplemented with 5% fetal bovine serum, penicillin (100 units/ml), and proportion of cancer patients. Whereas it has been long recognized that streptomycin (100 mg/ml) at 37°C in humidified air containing 5% CO2. The the effectiveness of anticancer drugs can vary significantly among indi- cell line panel has been used in anticancer drug screening programs at vidual patients, the same protocols are often applied without consider- the Japanese Foundation for Cancer Research (2) and for modeling systems at ation of the different cancer cell characteristics of each patient. Because the Developmental Therapeutics Program of the NCI (4) and has been examined for susceptibility to a number of chemical compounds, including current at present, there is no way to predict the effectiveness of a cancer anticancer drugs. chemotherapy for each case, cancer patients are still treated with the same Growth Inhibition Assay and Data Processing. Growth inhibition was protocols despite the possibility that the treatment may not be appropriate. assessed as changes in total cellular protein after 48 h of drug treatment using a Two major factors need to be considered when regarding the efficacy sulforhodamine B assay. The GI50 was calculated as described previously (2, 4). of particular anticancer drugs: (a) local blood or tissue drug concentra- Identification of Gene Expression Profiles by cDNA Microarray. Cell tions can be influenced by the activities of metabolic enzymes, which lines grown as monolayers to log phase were washed twice with PBS, and total may activate or inactivate the drug, and actions of drug transporters; and RNA was extracted with TRIzol reagent (Life Technologies, Inc.). After treatment (b) differences in individual drug efficacies may also reflect the intrinsic with DNase I (Boehringer Mannheim) to remove contaminating genomic DNA, susceptibility of cancer cells to those anticancer drugs. In the latter case, poly(A)ϩ RNA was extracted using an mRNA Purification Kit (Amersham a number of genes has been reported to influence chemosensitivity, e.g., Pharmacia Biotech). Probe cDNA-labeling reactions for each sample were performed as described previously (5), except that an oligodeoxythymidylate primer cancer cells expressing high levels of P-glycoprotein encoded by the was used instead of random hexamers. Each sample was reverse transcribed in the ABCB1 (formerly MDR-1) gene are often resistant to a large subset of presence of Cy5-labeled dCTP. A mixture of mRNA from all 39 cell lines was anticancer drugs, including vincristine, etoposide, and paclitaxel (taxol; prepared as the control probe. The mRNA mixture was amplified by T7-based Ref. 1). However, it has become obvious that the susceptibility of cancer amplification and reverse transcribed in the presence of Cy3-labeled dCTP, as cells to particular anticancer drugs cannot be predicted by a single factor described previously (5–7). Sample and control-labeled probes were mixed to- but is determined by many factors that influence overall sensitivity. gether and hybridized to cDNA microarray slides that contained 9216 human Therefore, to establish an appropriate protocol for the prediction of genes (3). Hybridized slides were scanned, and the fluorescence intensities of Cy5 chemosensitivity, we need to accumulate information on the sets of genes (sample) and Cy3 (control) for each gene spot quantified by using a GenIII that pharmacologically characterize cancer cells on treatment with par- microarray scanner (Amersham Pharmacia Biotech) with Array Vision software ticular anticancer drugs. (Imaging Research, Inc.). Each slide contained 52 housekeeping genes to normal- ize the signal intensities of the fluorescent dyes. The intensities of Cy5 and Cy3 were adjusted so that the mean Cy5 and Cy3 intensities of probe cDNA binding Received 7/2/01; accepted 12/10/01. to the housekeeping genes were equivalent. 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 Classification of Anticancer Drugs According to Drug Activity Pattern 18 U.S.C. Section 1734 solely to indicate this fact. Against Human Cancer Cell Lines. A hierarchical clustering method was 1 Supported by Grant-in-Aid for Scientific Research on Priority Areas (C) from the applied to the chemosensitivity data. Before applying the clustering algorithm, Ministry of Education, Culture, Sports, Science and Technology, Japan. 2 To whom requests for reprints should be addressed, at Division of Molecular 3 Pharmacology, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, The abbreviations used are: NCI, National Cancer Institute; GI50, drug concentration 1-37-1 Kami-Ikebukuro, Toshima-ku Tokyo 170-8455, Japan. Phone: 81-3-5394-4068; required for 50% growth inhibition; topo, DNA topoisomerase; TYMS, thymidylate Fax: 81-3-3918-3716; E-mail: [email protected]. synthetase. 1139

Խ Խ the GI50 values were log transformed, and the absolute values ( log10GI50 ) Table 1 Anticancer drugs used in analysis were used for correlation analysis. Correlations were assessed by the Pearson Type Drug name Target/mode of action correlation coefficient as described previously (2, 8). Analyses were performed Vinca alkaloids Camptothecin Topo I using “Gene Spring” software (Silicon Genetics). Irinotecan Topo I Classification of Human Cancer Cell Lines According to Gene Expres- SN-38 Topo I sion Profiles. A hierarchical clustering method was applied to the gene Vincristine Tubulin expression data. To obtain reproducible clusters, we selected only the 991 Vinblastine Tubulin Ͼ Vindecine Tubulin genes that passed the cutoff filter [control Cy3 signals were 50,000 relative Taxanes Paclitaxel Tubulin fluorescent units in Ͼ80% of cases, i.e., 32 of 39 cell lines examined; the Docetaxel Tubulin fluorescence ratio (Cy5:Cy3) was greater than the mean ratio by at least 5-fold Antibiotics Actinomycin D RNA synthesis in at least 3 of 39 cell lines]. Analysis was performed using Gene Spring. Bleomycin DNA synthesis Peplomycin DNA synthesis Before applying the clustering algorithm, the fluorescence ratio for each spot Mitoxantrone DNA synthesis was log transformed (log2Cy5:Cy3), and the data for each sample centered to Mitomycin C DNA alkylator remove experimental biases. Correlations were assessed by the Pearson cor- Anthracyclines Epirubicin DNA synthesis/Topo II relation coefficient as described (9, 10). Doxorubicin DNA synthesis/Topo II Daunorubicin DNA synthesis/Topo II Correlation Analysis between Gene Expression Profiles and Chemosen- Pirarubicin DNA synthesis/Topo II sitivity Profiles. We calculated the degree of similarity between drug activity Aclarubicin DNA/RNA synthesis and gene expression pattern using the Pearson correlation coefficient by the Antimetabolites Methotrexate DHFRa following formula: 10EdAM DHFR 5-fluorouracil Pyrimidine Doxifluridine Pyrimidine ⌺͑ Ϫ ͒͑ Ϫ ͒ xi xm yi ym Carmofur Pyrimidine r ϭ ͱ͑ Ϫ ͒2 ⌺͑ Ϫ ͒2 Tegafur Pyrimidine xi xm yi ym Cytarabine Pyrimidine Gemcitabine Pyrimidine where xi represented the log expression ratio (log2Cy5:Cy3) of gene x in cell Enocitabine Pyrimidine Խ Խ i, whereas yi was the log sensitivity ( log10GI50 ) of cell i to drug y. xm 6-thioguanine Purine 6-mercaptopurine Purine represented the mean of the log expression ratio of gene x, and ym represented Խ Խ DNA alkylators Cisplatin DNA cross-linker the mean sensitivity ( log10GI50 ) of the drug. In this analysis, we selected Carboplatin DNA cross-linker genes that passed the cutoff filter (signal intensities were Ͼ25,000 relative Oxaliplatin DNA cross-linker fluorescent units or signal:noise ratios were Ͼ3 in either Cy3 or Cy5 in Ͼ80% Busulfan DNA cross-linker of cases, i.e., 32 of 39 cell lines examined). We then selected genes with Melphalan DNA cross-linker Thiotepa DNA cross-linker expression patterns that showed significant correlation to drug activity pat- Dacarbazine DNA synthesis terns. A significant correlation was defined as a P Ͻ 0.05 and a slope of the Carboquone DNA synthesis Ͼ Խ Խ regression line 1.5, where the difference of the log10GI50 values between Nitrogen mustard DNA synthesis the most and the least sensitive cell lines was fixed as 1. Other DNA-damaging agents Etoposide Topo II Amsacrine Topo II Clustering Analysis of Drug and Gene Profiles on the Basis of Pearson Genistein Topo II Correlation Coefficients between Drugs and Genes. We performed cluster- Ellipticine Topo II ing analysis on the drugs and genes on the basis of the Pearson correlation ICRF154 Topo II coefficients, as described previously (10). We used all 1071 genes that passed Neocarzinostatin DNA synthesis Cladribine DNA synthesis the selection criteria described in the previous section for at least 1 of 55 drugs. Tomudex DNA synthesis The algorithm used to calculate the correlation between drug a and drug b was Hormone analogues Tamoxifen Estrogen receptor the standard correlation coefficient by the following formula: Toremifene Estrogen receptor Estramustine Estradiol/nitrogen mustard Interferons Interferon-␣ Biological response modifier Interferon-␤ Biological response modifier ⌺aibi r ϭ Interferon-␥ Biological response modifier ͱ 2 2 Others STI 571 Abl kinase inhibitor ⌺ai ⌺bi L-asparaginase Protein synthesis PSC833 P-glycoprotein where ai represented the Pearson correlation coefficient between drug a and a DHFR, dihydrofolate reductase; 10EdAM, 10ethyl-lodeaza-aminopterin. gene i, whereas bi was the coefficient between drug b and gene i. Similarly, the algorithm used to calculate the correlation between gene c and gene d was the standard correlation coefficient by the following formula: clusters, each consisting of drugs with similar modes of action, e.g., one cluster included topo I inhibitors, such as camptothecin, irinotecan, and ⌺cjdj r ϭ SN-38. The second cluster consisted of doxorubicin and other anthracy- ͱ⌺c 2 ⌺d 2 j j cline compounds. 5-fluorouracil and its derivatives also clustered into a single group. Interestingly, actinomycin D, which is not known to act on where cj represented the Pearson correlation coefficient between gene c and drug j, whereas dj was the coefficient between gene d and drug j. tubulin, clustered with tubulin binders, including taxanes and Vinca alkaloids, suggesting actinomycin D might partly share the mode of RESULTS action with these drugs. Indeed, both actinomycin D and Vinca alkaloids were shown previously to inhibit RNA synthesis in the cells. These Developing Gene Expression and Chemosensitivity Databases results indicated that our system using the 39 cancer cell lines was able Using 39 Human Cancer Cell Lines. We examined the growth in- to classify drugs on the basis of mechanism of action. On the other hand, hibitory effect of 55 current anticancer drugs (Table 1) on 39 human clustering analysis performed on the cancer cell lines according to che- cancer cell lines used to evaluate the efficacy of novel anticancer com- mosensitivity data did not always reflect the tissue of origin, in agreement pounds (2). The sensitivity of each of the 39 cell lines to each drug was with previous findings (10). assessed by GI50. The GI50 values were log transformed, and the absolute We then performed cDNA microarray analysis to investigate the Խ Խ values ( log10GI50 ) were used for further analysis. Hierarchical cluster- expression profiles of 9216 genes in each of the 39 cell lines. The ing was performed on the 55 anticancer drugs based on their effect on the expression of each gene was assessed by the ratio of expression level 39 cell lines (Fig. 1). As a result, the 55 drugs were classified into several in the sample against a universal control, and the values log trans- 1140

Fig. 1. Two-dimensional hierarchical clustering was applied to growth inhibitory activity data of 55 anticancer drugs determined in 39 cell lines. Whereas drugs with similar mechanisms of action tended to cluster together, cell lines from the same tissue of origin did not. Cell line names are colored to reflect the tissue of origin. The tissue of origin of each cell line is described in the abbreviated symbols: LU, lung cancer; CO, colorectal cancer; GA, gastric cancer; OV, ovarian cancer; BR, breast cancer; GL, glioma; RE, renal cancer; ME, melanoma; PR, prostate cancer. Colors on the imaging map represent the relative sensitivity of the cell line against the drug. Numbers beside the drug names refer to the difference Խ Խ Խ Խ in log10GI50 from the mean log10GI50 for each drug, e.g., the cell line with 3.0 (in red) represents 1000-fold more sensitivity than the mean.

formed before analysis [log2 (sample)/(control)]. We then applied the hierarchical clustering method to the cell line data. Cell lines with the same tissue of origin tended to form clusters, except for breast and ovarian cancers (Fig. 2). We examined the expression profiles of the cell lines NCI-H23, OVCAR3, OVCAR5, and OVCAR8 twice using independent mRNA preparations and confirmed that the four cell lines clustered very closely with each other, indicating the reproducibility of our experimental procedure (data not shown). Correlation Analysis between Drugs and Genes Using an Inte- grated Database of Gene Expression and Chemosensitivity. To screen for genes that may be involved in chemosensitivity, we integrated the two databases and performed correlation analysis between the gene expression and drug sensitivity datasets. Comprehensive calculations for Pearson correlation coefficients were performed on the gene expression and drug sensitivity patterns of the 39 cell lines. Genes were selected that exhibited significant correlations that satisfied the following criteria: a P of the correlation Ͻ0.05 and a slope of the regression line Ͼ1.5, where the difference of the log10 GI50 values between the most and the least sensitive cell lines was fixed as 1. As a result, different sets of genes were Fig. 2. Hierarchical clustering was applied to cell lines on the basis of expression data extracted with respect to each of the 55 drugs tested (data not shown). We from a set of 991 cDNAs measured across 39 cell lines. The 991 cDNAs were selected then examined for genes that were associated with Ն10 of the 55 drugs from a total of 9216 genes by criteria described in “Materials and Methods.” 1141

Fig. 3. Genes that significantly correlated with multiple drugs. Genes that correlated with Ն10 drugs are shown. Multiplicity is noted on the left of the gene symbols; the order of drugs is the same as for Fig. 4. Red, a positive correlation; green, a negative correlation. The shade of the color represents the P of the correlation. and found 50 genes that were significantly correlated to sensitivity to a (DDB2), involved in DNA repair triggered by UV radiation (12), showed relatively wide range of anticancer drugs (Fig. 3). Aldose reductase a positive correlation to 20 drugs. These genes were suggested to be (AKR1B1), which catalyzes the conversion of glucose to sorbitol (11), common predictive markers of chemosensitivity. LIM domain kinase 2 was most commonly associated with drug sensitivity, revealing a signif- (LIMK2), involved in actin skeleton remodeling (13), showed a negative icant correlation with 24 drugs. Damage-specific DNA binding protein 2 correlation to 18 drugs, suggesting that LIMK2 may be a common 1142

Fig. 4. Two-dimensional clustering analysis of genes and drugs on the basis of Pearson correlation coefficients (see “Materials and Methods”). Each row, a drug; each column, a gene. Colors are same as used in Fig. 3. As for clustering analysis based on drug activity data, drugs with similar modes of action clustered.

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Fig. 5. Gene sets that commonly correlated with drugs with similar mechanisms of action. Colors are same as used in Fig. 3. The drugs examined were as follows: camptothecin and its derivatives irinotecan and SN-38 (A); bleomycin and its derivative peplomycin (B); anthracycline compounds doxorubicin, epirubicin, and daunorubicin (C); and 5-fluorouracil and its derivatives carmofur, tegafur, and doxifluridine (D). We selected genes with common significant correlations to two of two drugs (B) or two or more drugs of three (A and C) or four (D). 1144

Fig. 5D. Continued. predictive marker of drug resistance. Interestingly, whereas overall pro- “Materials and Methods,” data not shown). Over 50,000 (1,071 ϫ 55) files of gene expression in cancer cells reflected their tissues of origin, correlations were calculated and clustered. Similar to the clustering these genes are not strongly associated with tissue of origin (data not analysis performed using drug sensitivity data, sets of drugs with shown). similar mechanisms of action tended to cluster with each other; Correlation of Gene Expression and Drug Sensitivity on the however, some clusters were reorganized (Figs. 1 and 4). The result Basis of Pearson Correlation Coefficient between Genes and indicated that the correlating gene sets tended to overlap with respect Drugs. To clearly identify correlations between gene expression pro- to drugs with similar mechanisms of action. files and the sensitivity to anticancer drugs, we applied a hierarchical Putative Genes Involved in Chemosensitivity. We then searched clustering method on both drugs and genes, based on calculations of for genes that correlated with clusters of drugs with similar mecha- Pearson correlation coefficients between drug efficiency and gene nisms of action. For the 5-fluorouracil derivatives, we identified genes expression (Fig. 4). This data mining method was first developed by that revealed significant correlations with at least two of the four Scherf et al. (10, 14). We used 1,071 genes that revealed significant drugs, 5-fluorouracil, carmofur, tegafur, and doxifluridine, as shown correlations, by the criteria mentioned above, to at least one drug (see in Fig. 5D. Among the 51 genes that showed a negative correlation, 1145

Downloaded from cancerres.aacrjournals.org on October 2, 2021. © 2002 American Association for Cancer Research. CHEMOSENSITIVITY AND GENE EXPRESSION OF CANCER CELLS the gene encoding cyclin C (CCNC) exhibited a broad negative mode of action. The previous study by Scherf et al. (10) showed correlation, not only with 5-fluorouracil derivatives but also with topo expected correlations, such as that between 5-fluorouracil and dihy- inhibitors, anthracyclines, antifolates, and tubulin binders. In contrast, dropyrimidine dehydrogenasae (DPYD). A moderately negative cor- the genes coding KIAA0097 protein and survivin (BIRC5) showed relation between 5-fluorouracil and TYMS was demonstrated in our specific correlation only to fluoropyrimidines. Genes with positive study (r ϭϪ0.29, P Ͻ 0.1). In addition, a stronger negative corre- correlations included three aldo-keto reductase family members lation between 5-fluorouracil derivative carmofur and TYMS was also (AKR1C1, AKR1C3, and AKR1B11), two aldehyde dehydrogenases observed (r ϭϪ0.38, P Ͻ 0.01; data not shown). Furthermore, (ALDH1 and ALDH3), and galectin 4 (LGALS4, in duplicate). positive correlations between the DNA topo II inhibitor etoposide and We performed similar analyses with respect to other sets of drugs, DNA topo II ␣ (TOP2A, r ϭ 0.39, P Ͻ 0.05) and ␤ (TOP2B, r ϭ 0.29, such as topo I inhibitors (camptothecin, irinotecan, and SN-38; Fig. P Ͻ 0.1) were also found (data not shown). Although the Pearson 5A), bleomycin derivatives (bleomycin and peplomycin; Fig. 5B), and correlation coefficients were not high, together these moderate corre- anthracyclines (doxorubicin, epirubicin, and daunorubicin; Fig. 5C). lations might be sufficient as the sensitivity of cancer cells to anti- As a result, we generated different sets of genes that revealed positive cancer drugs appears not to be determined by the expression of a and negative correlations with respect to different clusters of drugs. Of single gene. these, topo I inhibitors, bleomycin derivatives, and anthracyclines Clustering analysis of cell lines revealed that overall profiles of closely clustered with each other on the basis of both drug activity and gene expression in cancer cells reflected their tissues of origin (Fig. Pearson correlation coefficient, suggesting these compounds act by 2). Therefore, some of the retrieved genes preferentially expressed in similar mechanisms (Figs. 1 and 4), e.g., genes encoding 14-3-3 ␴ cells derived from specific tissues, e.g., keratin genes related to (SFN), LIM domain kinase 2 (LIMK2), and cathepsin H (CTSH) epithelial cell type (data not shown). Similarly, some of the drugs used commonly showed negative correlations, whereas genes encoding in our study tended to show tissue-oriented efficacy (Fig. 1). There- DDB2 (DDB2) and the ALL-fused gene from chromosome1q (AF1Q) fore, some of the genes reflecting cells’ tissue of origin correlating revealed positive correlations (Fig. 5, A–C). However, we also ob- with chemosensitivity might be associated with sensitivity to such served some differences, e.g., keratins (KRT7–8 and -19) correlated drugs. On that occasion, the correlation should be validated within with camptothecins and bleomycin derivatives but not with anthracy- each set of cell lines derived from the same tissues. On the other hand, clines, whereas expression of the gene encoding transducer of ErbB2 there was a substantial population of genes that was not strongly 1(TOB1) positively correlated with anthracycline but not with bleo- associated with tissue of origin, including AKR1B1, LIMK2, and mycin or camptothecins. Our results suggested that drugs with similar DDB2 (data not shown). These genes are conceivably good candidates mechanisms of action shared sets of genes involved in cancer cell for predictive markers of drug efficacy. susceptibility and that these gene sets varied with respect to clusters of Clustering analysis of the drugs on the basis of Pearson correlation drugs with similar mechanisms of action. coefficients between drug activity and gene expression levels revealed that drugs with similar modes of action clustered into the same groups DISCUSSION and that the sets of chemosensitivity-associated genes were similar between each drug within a group (Fig. 4). Thus, we were able to In this study, we investigated the gene expression profiles of 39 retrieve genes that commonly correlated within each of the groups of human cancer cell lines that were pharmacologically characterized on drugs with similar functions (Fig. 5, A–D, data not shown), e.g., we treatment with various anticancer drugs, using a cDNA microarray identified the genes encoding survivin (BIRC5) and C-IAP1 of apop- system consisting of 9216 genes. In conjunction with chemosensitiv- tosis1 (BIRC2) that revealed negative correlations with 5-fluorouracil ity data, we identified genes expressed in the 39 cell lines with derivatives, although survivin showed either no significant negative expression patterns that correlated significantly with the susceptibility correlations or moderately positive correlations to the other genotoxic profiles of each of the 55 anticancer drugs evaluated. Positively anticancer drugs (Fig. 5D). The inhibitor of apoptosis family of correlating genes are likely to reflect chemosensitivity as the higher proteins, including survivin, has been shown to play important roles in the expression level of these genes, the lower the GI50. Similarly, the suppression of apoptosis and is conceivably a chemoresistance genes that showed negative correlations are likely to reflect chemore- factor (15, 16). Thus, the most important aspect of our analysis is that sistance. Thus, the expression levels of these genes may be useful as we were able to retrieve different gene sets that may be used as predictive markers of drug effectiveness. putative diagnostic markers for chemosensitivity prediction with re- Our database, comprising both gene expression data and drug spect to each class of drugs with similar mechanisms of action. The activity data for the same set of cell lines, was first developed by Ross next step will be to confirm the predictive power of our findings. In et al. and Scherf et al. (9, 10) using 60 human cancer cell lines a recently published study, Staunton et al. (17) identified putative (NCI60). These studies examined gene expression profiles in 60 cell predictive markers of chemosensitivity and showed the feasibility of lines that were pharmacologically characterized on treatment with chemosensitivity prediction by transcriptional profiling. In our current various kinds of anticancer drugs. The number of cell lines used in the study, using a different methodology, we could narrow down the present study was 39. Of these, 9 cell lines (6 derived from gastric number of candidate genes from thousands to dozens that predict cancers and 3 from breast cancers) were not included in the NCI60 chemosensitivity, although we have not completed to evaluate our panel (4). Using this different set of 39 cell lines, we performed findings. The validation studies using dozens of cell lines outside the hierarchical clustering with respect to 55 anticancer drugs on the basis panel are under way. of growth inhibition and Pearson correlation coefficients between In summary, we have identified different sets of genes that may act drugs and genes. As shown in Figs. 1 and 4, drugs with similar as predictive markers for chemosensitivity to drugs with similar mechanisms of action clustered most of the time, in agreement with mechanisms of action. Data-mining methodologies described here and results of an earlier study that used a different set of cell lines (10). elsewhere (17) may be useful to develop systems to select suitable Moreover, we have shown previously that the mode of action of a chemotherapies in the future. However, statistical correlations are not novel compound can be predicted by examining its growth inhibitory the proof of causal relationships between gene expression and che- activities against the panel of 39 cell lines (2, 8). This indicated that mosensitivity. If these causal relationships can be demonstrated ex- our cell line panel had sufficient power to classify drugs according to perimentally, the resultant gene products may explain the mechanisms 1146

Downloaded from cancerres.aacrjournals.org on October 2, 2021. © 2002 American Association for Cancer Research. CHEMOSENSITIVITY AND GENE EXPRESSION OF CANCER CELLS of anticancer drugs. In addition, to elucidate the mechanisms of 6. Luo, L., Salunga, R. C., Guo, H., Bittner, A., Joy, K. C., Galindo, J. E., Xiao, H., anticancer drugs, it is also necessary to determine gene expression Rogers, K. E., Wan, J. S., Jackson, M. R., and Erlander, M. G. Gene expression profiles of laser-captured adjacent neuronal subtypes. Nat. Med., 5: 117–122, 1999. changes after administration of drugs (18). These studies will help us 7. Van Gelder, R. N., von Zastrow, M. E., Yool, A., Dement, W. C., Barchas, J. D., and to discover new targets of cancer chemotherapy, as well as predictive Eberwine, J. H. Amplified RNA synthesized from limited quantities of heterogeneous cDNA. Proc. Natl. Acad. Sci. USA, 87: 1663–1667, 1990. markers for conventional drugs. 8. Naasani, I., Seimiya, H., Yamori, T., and Tsuruo, T. FJ5002: a potent telomerase inhibitor identified by exploiting the disease-oriented screening program with COM- PARE analysis. Cancer Res., 59: 4004–4011, 1999. ACKNOWLEDGMENTS 9. Ross, D. T., Scherf, U., Eisen, M. B., Perou, C. M., Rees, C., Spellman, P., Iyer, V., Jeffrey, S. S., Van de Rijn, M., Waltham, M., Pergamenschikov, A., Lee, J. C., We thank Dr. Jun-ichi Okutsu for helpful discussions and Hiroko Bando, Lashkari, D., Shalon, D., Myers, T. G., Weinstein, J. N., Botstein, D., and Brown, Noriko Nemoto, and Noriko Sudo for the fabrication of cDNA microarray. We P. O. Systematic variation in gene expression patterns in human cancer cell lines. Nat. Genet., 24: 227–235, 2000. also thank Yumiko Nishimura, Mariko Seki, Keiko Sato, Fujiko Ohashi, Yoko 10. Scherf, U., Ross, D. T., Waltham, M., Smith, L. H., Lee, J. K., Tanabe, L., Kohn, Yoshida, Naohide Sunose, Sachiko Udagawa, and Akiko Kuramochi-Komi for K. W., Reinhold, W. 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Shingo Dan, Tatsuhiko Tsunoda, Osamu Kitahara, et al.

Cancer Res 2002;62:1139-1147.

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