Molecular Cancer Therapeutics
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Vol. 2, 199-205, February 2003     Molecular Cancer Therapeutics
© 2003 American Association for Cancer Research

Relevance Network between Chemosensitivity and Transcriptome in Human Hepatoma Cells1

Masaru Moriyama2, Yujin Hoshida, Motoyuki Otsuka, ShinIchiro Nishimura, Naoya Kato, Tadashi Goto, Hiroyoshi Taniguchi, Yasushi Shiratori, Naohiko Seki and Masao Omata

Department of Gastroenterology, Graduate School of Medicine, 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 Research Center, Tokyo 135-0064 [S. N.]; and Department of Functional Genomics, Graduate School of Medicine, Chiba University, Chiba 260-8670, [S. N.], Japan

2 To whom requests for reprints should be addressed, at Department of Gastroenterology, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan. Phone: 81-3-3815-5411, extension 33056; Fax: 81-3-3814-0021; E-mail: moriyamam-int{at}h.u-tokyo.ac.jp


   Abstract
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 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Generally, hepatoma is not a chemosensitive tumor, and the mechanism of resistance to anticancer drugs is not fully elucidated. We aimed to comprehensively evaluate the relationship between chemosensitivity and gene expression profile in human hepatoma cells, by using microarray analysis, and analyze the data by constructing relevance networks.

In eight hepatoma cell lines (HLE, HLF, Huh7, Hep3B, PLC/PRF/5, SK-Hep1, Huh6, and HepG2), the baseline expression levels of 2300 genes were measured by cDNA microarray. The concentrations of eight anticancer drugs (nimustine, mitomycin C, cisplatin, carboplatin, doxorubicin, epirubicin, mitoxantrone, and 5-fluorouracil) needed for 50% growth inhibition were examined and used as a measure of chemosensitivity. These data were combined and comprehensive pair-wise correlations between gene expression levels and the 50% growth inhibition values were calculated. Significant correlations with significance were used to construct networks of similarity.

Fifty-two relations, including 42 genes, were selected. Among them, nearly 20% were various types of transporters, and most of them negatively correlated with chemosensitivity. Transporter associated with antigen processing 1 was associated with resistance to mitoxantrone, consistent with previous reports. Other transporters were not reported previously to associate with chemosensitivity. Resistance to doxorubicin and its analogue, epirubicin, were positively correlated with topoisomerase II ß expression, whereas it negatively correlated with expression of carboxypeptidases A3 and Z. Response to nimustine was associated with expression of superoxide dismutase 2.

Relevance networks identified several negative correlations between gene expression and resistance, which were missed by hierarchical clustering. Our results suggested the necessity of systematically evaluating the transporting systems that may play a major role in resistance in hepatoma. This may provide useful information to modify anticancer drug action in hepatoma.


   Introduction
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 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
Hepatoma is a major cause of death even in developed countries, and its incidence is increasing (1). Despite the progress of therapeutic technique (2), the efficacy of radical therapy is hampered by frequent recurrence and advance of the tumor (3). Although chemotherapy was initially expected to provide a breakthrough for the treatment of advanced hepatoma, the overall results were disappointing (4, 5). We cannot identify the few patients who will respond to chemotherapy, and we do not have clear explanation of the mechanism of resistance to treatment.

The development of hepatoma is a complicated process, associated with numerous genetic factors. Large-scale transcriptional profiling allows us to obtain large amounts of data about complicated biological processes. However, to interpret the result of DNA microarray experiments, we have to extract the biologically significant information from the many changes that are random or not related to the phenomena studied.

Relevance networks offer a method to construct networks of similarity with various type of information, including mutual information and the correlation status (6). In this study, we applied this methodology to provide new information on chemosensitivity in hepatoma.


   Materials and Methods
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 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
Hepatoma Cells.
Eight hepatoma cell lines (HLE, HLF, Huh7, 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.

Anticancer Drugs.
We tested eight drugs used currently for the treatment of hepatoma in clinical practice: an antimetabolite (5-FU 3), two platinum anticancer agents (CDDP and CBDCA), two alkylating agents (ACNU and MMC), and three (topo) II inhibitors (ADM, EpiADM, and MIT). CDDP, CBDCA, ADM, MMC, MIT, and 5-FU were purchased from Sigma Co. (St. Louis, MO). ACNU was obtained from Sankyo Co. (Tokyo, Japan). EpiADM was obtained from Pharmacia Co. (Peapack, NJ).

Measurement of Response to Drugs.
The response of each cell line to the anticancer drugs was determined as follows. Briefly, cells were grown in 24-well microplates (IWAKI Glass, Chiba, Japan) and exposed to the drugs for 48 h. Cell growth was assessed by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay. The drug concentration required to inhibit cell growth by 50% in comparison with untreated controls was designated as the growth inhibition activity of the drug (GI50). Data were averages of three independent experiments.

RNA Extraction.
Total cellular RNA was extracted using an acid guanidine isothiocyanate phenol-chloroform method according to the manufacturer’s instructions (ISOGEN Reagent; Nippon Gene Co., Tokyo, Japan). RNA was treated with RNase-free DNase to remove contaminating genomic DNA, and RNA integrity was confirmed by gel electrophoresis and ethidium bromide staining. Polyadenylic acid mRNA was obtained using the Oligotex-dT30 mRNA purification kit (TaKaRa Co., Tokyo, Japan).

cDNA Microarray.
We assessed gene expression using in-house cDNA microarray developed by ourselves in the Helix Research Institute (Chiba, Japan; Ref. 7). A total of 2300 named, sequence-validated human cDNAs (Research Genetics) were spotted onto carbodiimide-coated glass slides using a robot (SPBIO-2000; Hitachi Software Engineering Co., Tokyo, Japan).

Microarray experiments were performed as described previously (8), and for each gene, expression was compared with expression in normal human liver RNA (Clontech, Palo Alto, CA) using the Cy5:Cy3 ratios. To control for labeling differences, reactions were carried out at least in duplicate, and the fluorescent dyes were switched. Each pair of probes was hybridized to a separate microarray. We have confirmed previously the reproducibility of such experiments (7–10).

Microarray Data Analysis.
Spots were only included if their raw fluorescence intensities were at least 1.5 times the local background. We adjusted raw data of fluorescence ratios (Cy5:Cy3) by log transformation (base 2), median centering, and normalization. Subsequently, we filtered data on various genes for additional analyses. Genes were included if one or more cell lines had fluorescence ratios >=2.0 or <=0.5, and maximum minus minimum values of log fluorescent ratios >=1.0.

To ascertain that the range of values of each gene expression ratio was sufficient for subsequent analysis, an entropy value, H, was calculated using the following formula:

where p(x) is the probability that a value was within decile x of that gene expression ratio. Genes within the lowest 5% of entropy values were removed, to avoid falsely high values in subsequent correlation analyses, because of outliers.

Relevance Networks.
Using log-transformed expression ratios of selected genes and the GI50 values of the anticancer drugs, similarity metrics, R, were calculated in a comprehensive pair-wise manner as a square of the Pearson correlation coefficient with original sign (positive or negative). The correlation coefficient, r, was calculated by the following formula:

where xi represents the log expression ratio (log2Cy5:Cy3) of gene x in cell line i, whereas yi is the log response (log10GI50) to drug y in cell line i. To generate a reference distribution of R, measurements of expression ratios and GI50 values were randomly permuted 100 times, and calculated Rs were recorded. On the basis of these random data, we set threshold values of R for selection of genes with significance levels of P < 0.001, P < 0.01, and P < 0.05. We considered that for a given pair of genes, a relationship with R higher than the threshold value may represent a significant biological relationship. We then connected these genes with each other to form clusters or relevance networks of related genes and drugs. For these calculations, we developed custom Perl codes (included in bioperl release 1.2).4 Finally, the networks were visualized as graphs using a custom JAVA application. The functions of the selected genes were obtained from web-based databases, e.g., the National Center for Biotechnology Information-Locuslink,5 Gene Ontology,6 Kyoto Encyclopedia of Genes and Genomes,7 and other linked databases.


   Results
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 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
GI50 Values of Anticancer Drugs.
Table 1 summarizes the GI50 values of eight anticancer drugs in eight hepatoma cell lines. Drugs and their analogues (CDDP and CBDCA; ADM and EpiADM) showed relatively similar ranges and average GI50 values. Mitomycin-C and MIT showed largest and smallest variance, respectively. The entropy values of GI50 measurements range from 1.75 to 2.75, whereas, those of filtered genes range from 1.55 to 3.00 (the 5% percentile is 1.90), suggesting the entropy values of CBDCA, EpiADM, and mitomycin-C were relatively small compared with those of the filtered gene expression ratios.


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

 
Relevance Networks Between Gene Expression Ratios and the GI50 Values.
Among eight anticancer drugs and 42 genes, a total of 53 relations showed Rs above the threshold. Among them, 20 relationships (38%) showed significance at P < 0.001, and Fig. 1 illustrates these. Detailed properties of selected genes are shown in Table 2. As expected, drugs and their analogues (CDDP and CBDCA; ADM and EpiADM) were connected with each other. Nearly 20% of selected genes were involved in various types of transport and had negative correlation with the GI50 values, whereas ~6% of genes on our microarray encode transporters. Approximately 10% of selected genes were associated with cell cycle control and cell proliferation.



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  		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 < 0.001. Intermediate lines represent a significance level of P < 0.01. Thinnest lines represent a significance level of P < 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.

 

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  		Table 2 Genes associated with chemosensitivity

 
Several enzymes shared similar response to ADM and EpiADM: CPA3, CPZ, and TOP2B had similar correlation status between the two drugs. SOD2 was positively correlated with CDDP and negatively correlated with ACNU. TAP1 was positively correlated with MIT and negatively correlated with 5-FU. CA1 and TRG were negatively correlated with EpiADM.


   Discussion
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 Materials and Methods
 Results
 Discussion
References
 
In cancer, response to anticancer drugs might be determined by a delicate balance of various factors in complicated systems in cells, rather than by just one or a few such factors (11). To understand drug response, applying genome-wide simultaneous analysis is a logical approach. However, elucidating response mechanisms remains challenging. The cancer may be heterogeneous in location and time course for each tumor even in an individual, and its properties may be organ-specific. Aiming to get new insights into such a complicated mechanism, we used a relevance network of similarity based on DNA microarray data to study hepatoma.

Previous microarray research on this disease showed that some growth factors and detoxification-related genes might be associated with its cancer development (12–18). In comparison, our current study suggested that low levels of expression of specific intra- and intercellular transporters might be associated with its resistance to drugs. Most of the transporters showed negative correlation with the GI50 values, and these correlations were not identified by hierarchical clustering. The gene expression profiles of these transport systems may be an efficient and practical way to understand chemosensitivity, because most of the genes code for membrane proteins, and it is difficult to analyze their function individually (in fact, none of them is included in the Protein Data Bank entry).

Our analysis also picked up several genes reported previously to be associated with response to anticancer drugs. TOP2B was associated with resistance to two anthracyclines, ADM and EpiADM. The {alpha} isoform of TOP2 is a target for anthracycline and anthracenedione, and its transcription was positively correlated with resistance to topo II inhibitors. The association between TOP2B expression and the resistance of cancers to topo II inhibitors correlated positively with susceptibility to topo II inhibitors, whereas the association between TOP2B expression and the resistance to cancers to topo II inhibitors varies among different organs and different drugs (11). In our experiments, TOP2B expression was positively correlated with resistance to ADM and EpiADM.

Expression of two carboxypeptidases, CPA3 and CPZ, was negatively correlated with resistance to ADM and EpiADM. A previous study showed that carboxypeptidase G2, an enzyme that hydrolyzes the COOH-terminal glutamate residue, improves response to another anticancer drug, methotrexate (19). It may be possible that in hepatoma, CPA3 and CPZcould decrease resistance to ADM and EpiADM.

CA1, which was reportedly associated with colon cancer development (20), showed negative correlation with resistance to EpiADM. TRG also showed negative correlation with resistance to EpiADM. This is consistent with prior reports that polymorphisms in TRG are associated with early onset colorectal cancer (21).

Superoxide, a toxic free radical, reportedly plays an important role in anticancer effects of anthracycline (11). SOD2 inactivates this free radical, but in this study its expression was not associated with resistance to the anthracycline derivates tested. On the other hand, SOD2 expression is negatively correlated with resistance to nitrosourea in various cancer cells (22). Our data also indicated a negative correlation between SOD2 expression and resistance to ACNU, also a nitrosourea.

TAP1, a member of multidrug resistance family, is associated with resistance to MIT (23). Consistent with this, we found that TAP1 expression showed a specific positive correlation with MIT GI50.

Cluster analysis does not always provide a complete interpretation of microarray data, and the application of more than one analysis technique can illuminate different relationships (24). Relevance network analysis can capture negative correlations that are missed in hierarchical clustering and simultaneously display them with positive correlations. On the other hand, relevance network analysis has several limitations. First, large-scale networks may not be detected if individual links are weak, because during analysis all of the links that are subthreshold are discarded. Second, the analysis may not necessarily assign equal statistical weight to all of the observations. Variances of measurements are inhomogeneous; that is, there may be very little variation in most of the samples and great variation in only a few of the samples, and the analysis may give greater weight to the latter. Third, this is not a globally optimal network. Fourth, comprehensive pair-wise calculation of the dissimilarity measure requires extensive computing resources (25).

In summary, our results highlighted the importance of various types of transporters that may affect chemosensitivity in hepatoma. However, most of them are ubiquitous molecules, and, thus, a study of these alone will not allow us to understand whole mechanism of chemosensitivity. Establishment of systems biology models, e.g., focused on transport systems rather than one or two individual transporters, will help to understand anticancer drug action in hepatoma (26).


   Footnotes
 
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.

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 Education, Culture, Sports, Science and Technology, Japan. Back

3 The abbreviations used are: 5-FU, 5-fluorouracil; ACNU, nimustine; ADM, doxorubicin; CBDCA, carboplatin; CDDP, cisplatin; CA1, carbonic anhydrase 1; CPA3, carboxypeptidase A3; CPZ, carboxypeptidase Z; EpiADM, epirubicin; GI50, 50% growth inhibition activity of the drug; topo, topoisomerase; MIT, mitoxantrone; MMC, mitomycin C; SOD, superoxide dismutases; TAP, transporter associated with antigen processing; TRG, T-cell receptor {gamma} locus; TOP2B, DNA topoisomerase II ß. Back

4 Internet address: http://www.bioperl.org/. Back

5 Internet address: http://www.ncbi.nih.gov/. Back

6 Internet address: http://www.geneontology.org/. Back

7 Internet address: http://www.genome.ad.jp/kegg/. Back

Received 12/ 2/02; accepted 12/ 2/02.


   References
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 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

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