Relevance Network Between Chemosensitivity and Transcriptome in Human Hepatoma Cells1

Home , CPZ (gene), KIFAP3, Metalloexopeptidase, Nimustine

Vol. 2, 199–205, February 2003 Molecular Cancer Therapeutics 199

Relevance Network between Chemosensitivity and Transcriptome in Human Hepatoma Cells1

Masaru Moriyama,2 Yujin Hoshida, topoisomerase II ␤ expression, whereas it negatively Motoyuki Otsuka, ShinIchiro Nishimura, Naoya Kato, correlated with expression of carboxypeptidases A3 Tadashi Goto, Hiroyoshi Taniguchi, and Z. Response to nimustine was associated with Yasushi Shiratori, Naohiko Seki, and Masao Omata expression of superoxide dismutase 2. Department of Gastroenterology, Graduate School of Medicine, Relevance networks identified several negative University of Tokyo, Tokyo 113-8655 [M. M., Y. H., M. O., N. K., T. G., H. T., Y. S., M. O.]; Cellular Informatics Team, Computational Biology correlations between gene expression and resistance, Research Center, Tokyo 135-0064 [S. N.]; and Department of which were missed by hierarchical clustering. Our Functional Genomics, Graduate School of Medicine, Chiba University, results suggested the necessity of systematically Chiba 260-8670 [N. S.], Japan evaluating the transporting systems that may play a major role in resistance in hepatoma. This may provide Abstract useful information to modify anticancer drug action in Generally, hepatoma is not a chemosensitive tumor, hepatoma. and the mechanism of resistance to anticancer drugs is not fully elucidated. We aimed to comprehensively Introduction evaluate the relationship between chemosensitivity and Hepatoma is a major cause of death even in developed gene expression profile in human hepatoma cells, by countries, and its incidence is increasing (1). Despite the using microarray analysis, and analyze the data by progress of therapeutic technique (2), the efficacy of radical constructing relevance networks. therapy is hampered by frequent recurrence and advance of In eight hepatoma cell lines (HLE, HLF, Huh7, Hep3B, the tumor (3). Although chemotherapy was initially expected PLC/PRF/5, SK-Hep1, Huh6, and HepG2), the baseline to provide a breakthrough for the treatment of advanced expression levels of 2300 genes were measured by hepatoma, the overall results were disappointing (4, 5). We cDNA microarray. The concentrations of eight cannot identify the few patients who will respond to chem- anticancer drugs (nimustine, mitomycin C, cisplatin, otherapy, and we do not have clear explanation of the mech- carboplatin, doxorubicin, epirubicin, mitoxantrone, and anism of resistance to treatment. 5-fluorouracil) needed for 50% growth inhibition were The development of hepatoma is a complicated process, examined and used as a measure of chemosensitivity. associated with numerous genetic factors. Large-scale tran- These data were combined and comprehensive pair- scriptional profiling allows us to obtain large amounts of data wise correlations between gene expression levels and about complicated biological processes. However, to interpret the 50% growth inhibition values were calculated. the result of DNA microarray experiments, we have to extract Significant correlations with significance were used to the biologically significant information from the many changes construct networks of similarity. that are random or not related to the phenomena studied. Fifty-two relations, including 42 genes, were Relevance networks offer a method to construct networks selected. Among them, nearly 20% were various types of similarity with various type of information, including mutual of transporters, and most of them negatively correlated information and the correlation status (6). In this study, we with chemosensitivity. Transporter associated with applied this methodology to provide new information on antigen processing 1 was associated with resistance to chemosensitivity in hepatoma. mitoxantrone, consistent with previous reports. Other transporters were not reported previously to associate Materials and Methods with chemosensitivity. Resistance to doxorubicin and Hepatoma Cells. Eight hepatoma cell lines (HLE, HLF, Huh7, its analogue, epirubicin, were positively correlated with Hep3B, PLC/PRF/5, SK-Hep1, Huh6, and HepG2) were obtained from RIKEN Cell Bank (Tsukuba, Japan). They were maintained using DMEM containing 10% fetal bovine serum. Received 12/2/02; accepted 12/2/02. Anticancer Drugs. We tested eight drugs used currently The costs of publication of this article were defrayed in part by the for the treatment of hepatoma in clinical practice: an antime- payment of page charges. This article must therefore be hereby marked 3 advertisement in accordance with 18 U.S.C. Section 1734 solely to indi- tabolite (5-FU ), two platinum anticancer agents (CDDP and cate this fact. 1 Supported in part by the Health Science Research Grants for Medical Frontier Strategy Research from the Ministry of Health, Labor, and Welfare of Japan, and by grants-in-aid for scientific research from the Ministry of 3 The abbreviations used are: 5-FU, 5-fluorouracil; ACNU, nimustine; Education, Culture, Sports, Science and Technology, Japan. ADM, doxorubicin; CBDCA, carboplatin; CDDP, cisplatin; CA1, carbonic 2 To whom requests for reprints should be addressed, at Department of anhydrase 1; CPA3, carboxypeptidase A3; CPZ, carboxypeptidase Z; Gastroenterology, Graduate School of Medicine, University of Tokyo, EpiADM, epirubicin; GI50, 50% growth inhibition activity of the drug; topo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan. Phone: 81-3-3815- topoisomerase; MIT, mitoxantrone; MMC, mitomycin C; SOD, superoxide 5411, extension 33056; Fax: 81-3-3814-0021; E-mail: moriyamam- dismutases; TAP, transporter associated with antigen processing; TRG, [email protected]. T-cell receptor ␥ locus; TOP2B, DNA topoisomerase II ␤.

CBDCA), two alkylating agents (ACNU and MMC), and three Relevance Networks. Using log-transformed expression (topo) II inhibitors (ADM, EpiADM, and MIT). CDDP, CBDCA, ratios of selected genes and the GI50 values of the antican- ADM, MMC, MIT, and 5-FU were purchased from Sigma Co. cer drugs, similarity metrics, R, were calculated in a com- (St. Louis, MO). ACNU was obtained from Sankyo Co. (To- prehensive pair-wise manner as a square of the Pearson kyo, Japan). EpiADM was obtained from Pharmacia Co. correlation coefficient with original sign (positive or negative). (Peapack, NJ). The correlation coefficient, r, was calculated by the following Measurement of Response to Drugs. The response of formula: each cell line to the anticancer drugs was determined as ϭ ͚ Ϫ Ϫ ͙͚ Ϫ 2 Ϫ 2 follows. Briefly, cells were grown in 24-well microplates r (xi xmean)(yi ymean)/ (xi xmean) (yi ymean) (IWAKI Glass, Chiba, Japan) and exposed to the drugs for where xi represents the log expression ratio (log2Cy5:Cy3) of 48 h. Cell growth was assessed by 3-(4,5-dimethylthiazol-2- gene x in cell line i, whereas yi is the log response (log10GI50) yl)-2,5-diphenyltetrazolium bromide assay. The drug con- to drug y in cell line i. To generate a reference distribution of centration required to inhibit cell growth by 50% in compar- R, measurements of expression ratios and GI50 values were ison with untreated controls was designated as the growth randomly permuted 100 times, and calculated Rs were re- inhibition activity of the drug (GI50). Data were averages of corded. On the basis of these random data, we set threshold three independent experiments. values of R for selection of genes with significance levels of RNA Extraction. Total cellular RNA was extracted using P Ͻ 0.001, P Ͻ 0.01, and P Ͻ 0.05. We considered that for an acid guanidine isothiocyanate phenol-chloroform method a given pair of genes, a relationship with R higher than the according to the manufacturer’s instructions (ISOGEN Rea- threshold value may represent a significant biological rela- gent; Nippon Gene Co., Tokyo, Japan). RNA was treated with tionship. We then connected these genes with each other to RNase-free DNase to remove contaminating genomic DNA, form clusters or relevance networks of related genes and and RNA integrity was confirmed by gel electrophoresis and drugs. For these calculations, we developed custom Perl ethidium bromide staining. Polyadenylic acid mRNA was codes (included in bioperl release 1.2).4 Finally, the networks obtained using the Oligotex-dT30 mRNA purification kit were visualized as graphs using a custom JAVA application. (TaKaRa Co., Tokyo, Japan). The functions of the selected genes were obtained from cDNA Microarray. We assessed gene expression using web-based databases, e.g., the National Center for Biotech- in-house cDNA microarray developed by ourselves in the nology Information-Locuslink,5 Gene Ontology,6 Kyoto En- Helix Research Institute (Chiba, Japan; Ref. 7). A total of cyclopedia of Genes and Genomes,7 and other linked data- 2300 named, sequence-validated human cDNAs (Research bases. Genetics) were spotted onto carbodiimide-coated glass slides using a robot (SPBIO-2000; Hitachi Software Engi- Results neering Co., Tokyo, Japan). GI50 Values of Anticancer Drugs. Table 1 summarizes the Microarray experiments were performed as described pre- GI50 values of eight anticancer drugs in eight hepatoma cell viously (8), and for each gene, expression was compared lines. Drugs and their analogues (CDDP and CBDCA; ADM with expression in normal human liver RNA (Clontech, Palo and EpiADM) showed relatively similar ranges and average Alto, CA) using the Cy5:Cy3 ratios. To control for labeling GI50 values. Mitomycin-C and MIT showed largest and differences, reactions were carried out at least in duplicate, smallest variance, respectively. The entropy values of GI50 and the fluorescent dyes were switched. Each pair of probes measurements range from 1.75 to 2.75, whereas, those of was hybridized to a separate microarray. We have confirmed filtered genes range from 1.55 to 3.00 (the 5% percentile is previously the reproducibility of such experiments (7–10). 1.90), suggesting the entropy values of CBDCA, EpiADM, Microarray Data Analysis. Spots were only included if and mitomycin-C were relatively small compared with those their raw fluorescence intensities were at least 1.5 times the of the filtered gene expression ratios. local background. We adjusted raw data of fluorescence ratios Relevance Networks Between Gene Expression Ratios (Cy5:Cy3) by log transformation (base 2), median centering, and and the GI50 Values. Among eight anticancer drugs and normalization. Subsequently, we filtered data on various genes 42 genes, a total of 53 relations showed Rs above the for additional analyses. Genes were included if one or more cell threshold. Among them, 20 relationships (38%) showed lines had fluorescence ratios Ն2.0 or Յ0.5, and maximum significance at P Ͻ 0.001, and Fig. 1 illustrates these. minus minimum values of log fluorescent ratios Ն1.0. Detailed properties of selected genes are shown in Table To ascertain that the range of values of each gene expres- 2. As expected, drugs and their analogues (CDDP and sion ratio was sufficient for subsequent analysis, an entropy CBDCA; ADM and EpiADM) were connected with each value, H, was calculated using the following formula: other. Nearly 20% of selected genes were involved in various types of transport and had negative correlation 10 with the GI50 values, whereas ϳ6% of genes on our ϭ Ϫ H ͚ p(x) log2 (p(x)) xϭ1 where p(x) is the probability that a value was within decile x of that gene expression ratio. Genes within the lowest 5% of 4 Internet address: http://www.bioperl.org/. 5 Internet address: http://www.ncbi.nih.gov/. entropy values were removed, to avoid falsely high values in 6 Internet address: http://www.geneontology.org/. subsequent correlation analyses, because of outliers. 7 Internet address: http://www.genome.ad.jp/kegg/.

Table 1 GI50 values of eight anticancer drugs in eight hepatoma cell linesa

Log10 GI50 value Drug Mechanism of action Hb Mean SD Range 5-FU DNA synthesis inhibitor 7.60 2.65 4.65–12.95 2.50 CDDP Platinum agent (alkylating) 7.39 3.90 3.77–15.57 2.16 CBDCA Platinum agent (alkylating) 6.33 3.65 3.84–14.90 1.75 ACNU Alkylating agent 5.00 1.05 3.60–6.30 2.25 MMC Alkylating agent 8.22 6.77 3.30–24.58 1.75 ADM topo II inhibitor (anthracycline) 6.32 2.52 3.51–10.52 2.75 EpiADM topo II inhibitor (anthracycline) 7.41 3.95 3.89–15.97 1.75 MIT topo II inhibitor (anthracenedione) 4.46 0.89 3.35–6.04 2.75 a GI50 values are log-transformed (base 10). b H, entropy value of the GI50 values for each drug.

Fig. 1. Relevance network between resistance to anticancer drugs and expression of various genes. Light blue nodes represent anticancer drugs. Yellow nodes represent genes of which the level of expression is significantly associated with resistance (GI50) to anticancer drugs. Red connecting lines indicate positive correlation, and blue connecting lines indicate negative correlation. The thickest lines represent a significance level of P Ͻ 0.001. Intermediate lines represent a significance level of P Ͻ 0.01. Thinnest lines represent a significance level of P Ͻ 0.05. Numbers attached to the connecting lines show the slope of the regression line. For detailed information about the genes shown, see Table 2.

microarray encode transporters. Approximately 10% of Discussion selected genes were associated with cell cycle control and In cancer, response to anticancer drugs might be determined cell proliferation. Several enzymes shared similar response to ADM and by a delicate balance of various factors in complicated sys- EpiADM: CPA3, CPZ, and TOP2B had similar correlation tems in cells, rather than by just one or a few such factors status between the two drugs. SOD2 was positively corre- (11). To understand drug response, applying genome-wide lated with CDDP and negatively correlated with ACNU. TAP1 simultaneous analysis is a logical approach. However, elu- was positively correlated with MIT and negatively correlated cidating response mechanisms remains challenging. The with 5-FU. CA1 and TRG were negatively correlated with cancer may be heterogeneous in location and time course for EpiADM. each tumor even in an individual, and its properties may be

Table 2 Genes associated with chemosensitivity

Downloaded from Drug Symbol Name Biological process Molecular function Cellular component Locus IDa

5-FU COL4A5 Collagen type IV ␣ 5 Structure Collagen Basement membrane Xq22 1287 CSK C-src tyrosine kinase Cell cycle, phosphorylation Tyrosine kinase Cytoplasm 15q23-q25 1445 GABRB1 GABA A receptor ␤1 Signaling small molecule GABA-inhibited Cl channel Membrane 4p12 2560 transport GMPR Guanosine monophosphate Cold response, nucleic acid GMP reductase — 6p23 2766 reductase metabolism GSTP1 Glutathione S-transferase ␲ Detoxification Glutathione S-transferase — 11q13 2950 mct.aacrjournals.org HPGD Hydroxyprostaglandin Electron transport Prostaglandin-D synthase — 4q34-q35 3248 dehydrogenase 15-(NAD) HXB Hexabrachion Adhesion Ligand binding/carrier Extracellular matrix 9q33 3371 SLC6A12 Solute carrier family 6 Small molecule transport GABA transporter Membrane 12p13 6539 member 12 SLC17A2 Solute carrier family 17 Sodium transport, phosphate Sodium phosphate symporter Membrane 6p21.3 10246 member 2 metabolism TAP1 Transporter 1 ATP-binding Defense, peptide transport ABC transporter Membrane 6p21.3 6890 cassette subfamily B CDDP SOD2 Superoxide dismutase 2 Oxidative stress response Manganese superoxide Mitochondrion 6q25.3 6648 on October 1,2021.© 2003American Associationfor Cancer dismutase ACNU BPI Bactericidal/permeability- Immunity Gram-negative bacterial binding Membrane 20q11.23-q12 671

Research. increasing protein CPZ Carboxypeptidase Z Proteolysis Metallocarboxypeptidase — 4p16.1 8532 NEDD4 Neural precursor cell exp. Sodium transport, ubiquitination, Ubiquitin-protein ligase Cytoplasm 15q 4734 dev. down-regulated 4 transcription NFX1 Nuclear transcription factor Inflammation, repression of Transcription factor Nucleus 9p13.1-p12 4799 X-box binding 1 transcription SLC26A2 Solute carrier family 26 Sulfate transport Sulfate transporter Membrane 5q31-q34 1836 member 2 SNX1 Sorting nexin 1 Endocytosis Degradation tagging Membrane 15q22.1 6642 SOD2 Superoxide dismutase 2 Oxidative stress response Manganese superoxide Mitochondrion 6q25.3 6648 dismutase TIMP2 Tissue inhibitor of Metalloprotease inhibitor Metalloprotease inhibitor — 17q25 7077 metalloproteinase 2 MMC ACADVL Acyl-Coenzyme A Energy, fatty acid ␤-oxidation Long-chain acyl-CoA Mitochondrion 17p13-p11 37 dehydrogenase very long dehydrogenase chain AGA Aspartylglucosaminidase Deglycosylation N4-(␤-N-acetylglucosaminyl)-L- Lysosome 4q32-q33 175 CXorf12 Chromosome X open reading ? ——Xq28 8269 frame 12 KNSL1 Kinesin-like 1 Mitosis Microtubule motor Kinesin, spindle 10q24.1 3832 PPP3CA Calcineurin A ␣ Signalling Phosphatase — 4q21-q24 5530 ZFP36L1 Zinc finger protein 36 C3H Growth ? Transcription factor Nucleus 14q22-q24 677 type-like 1 ADM CPA3 Carboxypeptidase A3 Proteolysis Secretory granule — 3q21-q25 1359 metalloexopeptidase CPZ Carboxypeptidase Z Proteolysis Metallocarboxypeptidase — 4p16.1 8532 SLC17A2 Solute carrier family 17 Sodium transport, phosphate Sodium phosphate symporter Membrane 6p21.3 10246 member 2 metabolism TOP2B Topoisomerase II ␤ DNA topology DNA topoisomerase Nucleus 3p24 7155 EpiADM BTD Biotinidase Biotin metabolism, differentiation, Biotin carboxylase — 3p25 686 development CA1 Carbonic anhydrase 1 Respiration, acid-base balance, Carbonate dehydratase Cytoplasm 8q13-q22.1 759 aqueous humor Downloaded from CD164 Sialomucin Adhesion, immunity, proliferation Adhesion of CD34ϩ cells Membrane 6q21 8763 inhibition CDC6 Cell division cycle 6 DNA replication inhibition, Nucleotide binding Nucleus, Cytoplasm 17q21.3 990 proliferation inhibition CHL1 Cell adhesion molecule Adhesion, signalling — Membrane —— (homolog of L1) CPA3 Carboxypeptidase A3 Proteolysis Secretory granule — 3q21-q25 1359 metalloexopeptidase

mct.aacrjournals.org CPZ Carboxypeptidase Z Proteolysis Metallocarboxypeptidase — 4p16.1 8532 CX3CR1 Chemokine (C-X3-C) Chemotaxis, defense, adhesion Chemokine receptor Membrane 3p21.3 1524 receptor 1 FOLR1 Folate receptor 1 Folate transport, endocytosis Tumor antigen, GPI-anchored Membrane 11q13.3-q14.1 2348 receptor KIFAP3 Kinesin-associated protein 3 Signalling, chromosome Linker ? Endoplasmic reticulum 1q23.2 22920 assembly MKI67 Antigen identified by Ki-67 Cell cycle, proliferation — Nucleus 10q25-qter 4288 NQO2 NAD(P)H dehydrogenase Electron transport NADPH dehydrogenase — 6pter-q12 4835 quinone 2 on October 1,2021.© 2003American Associationfor Cancer RBBP4 Retinoblastoma binding Proliferation inhibition Repressor of transcription Nucleus 1p34.3 5928 protein 4 SLC17A2 Solute carrier family 17 Sodium transport, phosphate Sodium phosphate symporter Membrane 6p21.3 10246 Research. member 2 metabolism TFAP2C Transcription factor AP-2 ␥ Oncogenesis, signalling, Transcription factor — 20q13.2 7022 transcription TOP2B Topoisomerase II ␤ DNA topology DNA topoisomerase Nucleus 3p24 7155 TRG@ T-cell receptor ␥ locus Immunity T-cell receptor — 7p15-p14 6965 MIT ACLY ATP citrate lyase ATP, citrate, CoA metabolism ATP-citrate (pro-S)-lyase Citrate lyase 17q12-q21 47 DUSP5 Dual specificity Heat shock response Protein tyrosine phosphatase — 10q25 1847 phosphatase 5 PSMC6 Proteasome 26S subunit Proteolysis Proteasome ATPase 26S proteasome 12q15 5706 ATPase 6 TAP1 Transporter 1 ATP-binding Defense, peptide transport ABC transporter Membrane 6p21.3 6890 cassette sub-family B a ID indicates the National Center for Biotechnology Information Locuslink ID. oeua acrTherapeutics Cancer Molecular 203 204 Network of Genes and Anticancer Drugs in Hepatoma

organ-specific. Aiming to get new insights into such a com- than one analysis technique can illuminate different relation- plicated mechanism, we used a relevance network of simi- ships (24). Relevance network analysis can capture negative larity based on DNA microarray data to study hepatoma. correlations that are missed in hierarchical clustering and Previous microarray research on this disease showed that simultaneously display them with positive correlations. On some growth factors and detoxification-related genes might the other hand, relevance network analysis has several lim- be associated with its cancer development (12–18). In com- itations. First, large-scale networks may not be detected if parison, our current study suggested that low levels of ex- individual links are weak, because during analysis all of the pression of specific intra- and intercellular transporters might links that are subthreshold are discarded. Second, the anal- be associated with its resistance to drugs. Most of the trans- ysis may not necessarily assign equal statistical weight to all porters showed negative correlation with the GI50 values, of the observations. Variances of measurements are inho- and these correlations were not identified by hierarchical mogeneous; that is, there may be very little variation in most clustering. The gene expression profiles of these transport of the samples and great variation in only a few of the systems may be an efficient and practical way to understand samples, and the analysis may give greater weight to the chemosensitivity, because most of the genes code for mem- latter. Third, this is not a globally optimal network. Fourth, brane proteins, and it is difficult to analyze their function comprehensive pair-wise calculation of the dissimilarity individually (in fact, none of them is included in the Protein measure requires extensive computing resources (25). Data Bank entry). In summary, our results highlighted the importance of var- Our analysis also picked up several genes reported previ- ious types of transporters that may affect chemosensitivity in ously to be associated with response to anticancer drugs. hepatoma. However, most of them are ubiquitous molecules, TOP2B was associated with resistance to two anthracy- and, thus, a study of these alone will not allow us to under- clines, ADM and EpiADM. The ␣ isoform of TOP2 is a target stand whole mechanism of chemosensitivity. Establishment for anthracycline and anthracenedione, and its transcription of systems biology models, e.g., focused on transport sys- was positively correlated with resistance to topo II inhibitors. tems rather than one or two individual transporters, will help The association between TOP2B expression and the resist- to understand anticancer drug action in hepatoma (26). ance of cancers to topo II inhibitors correlated positively with susceptibility to topo II inhibitors, whereas the association References between TOP2B expression and the resistance to cancers to 1. El-Serag, H. B., and Mason, A. C. Rising incidence of hepatocellular topo II inhibitors varies among different organs and different carcinoma in the United States. N. Engl. J. Med., 340: 745–750, 1999. drugs (11). In our experiments, TOP2B expression was pos- 2. Shiina, S., Teratani, T., Obi, S., Hamamura, K., Koike, Y., and Omata, itively correlated with resistance to ADM and EpiADM. M. 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Masaru Moriyama, Yujin Hoshida, Motoyuki Otsuka, et al.

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