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[Cancer Research 63, 2200-2205, May 1, 2003]
© 2003 American Association for Cancer Research

Molecular Biology and Genetics

Molecular Description of Evolving Paclitaxel Resistance in the SKOV-3 Human Ovarian Carcinoma Cell Line1

Diana E. Lamendola, Zhenfeng Duan, Rushdia Z. Yusuf and Michael V. Seiden2

Division of Hematology/Oncology, Massachusetts General Hospital Boston, Massachusetts 02114


   ABSTRACT
Top
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Ovarian cancer is currently the most lethal gynecological malignancy in the United States. Although effective therapies exist, the acquisition of multidrug resistance within persisting tumor cells renders curative therapies elusive for the majority of women with ovarian cancer. In an attempt to better define the evolution of paclitaxel resistance, three SKOV-3 sublines were selected during successive rounds of exposure to increasing paclitaxel concentrations. The sublines were selected to represent early (0.003 µM), intermediate (0.03 µM), and late (0.3 µM) paclitaxel resistance. RNA from these cell lines, SKOV-30.003TR, SKOV-30.03TR, and SKOV-30.3TR, as well as the parent cell line SKOV-3, was analyzed by cDNA array to evaluate transcript expression profiles. Arrays were performed using Affymetrix HG-U95Av2 arrays, which contain probes for ~9600 known human genes. Signal intensities were calculated by Microarray Suite 5.0 (Affymetrix, Santa Clara, CA). Expression patterns were analyzed by Affymetrix Data Mining Tool 3.0 with filtering of expression patterns for fold change in expression (maximum divided by minimum expression value/gene) and for variation of expression (maximum minus minimum expression value/gene). This analysis dismissed ~11,000 of ~12,000 expression patterns. The remaining ~1000 expression patterns were normalized and segregated into 20 partitions of a self-organizing map (SOM). The resulting SOM discriminates between genes, which are differentially expressed in early versus intermediate versus late paclitaxel resistance. For example, multidrug resistance 1 transcript expression is not elevated in SKOV-30.003TR as compared with parental SKOV-3 but demonstrates elevated expression in SKOV-30.03TR and SKOV-30.3TR. In contrast, SOM analysis demonstrates early (SKOV-30.003TR) transcriptional changes in a wide variety of genes, including gene families involved in cell growth/maintenance, cell structure, signal transduction, and inflammatory response. The use of array analysis with SOMs in sublines with progressive paclitaxel resistance can successfully define an evolution of resistance. Such an analysis may be useful at defining candidate gene families involved in the early-drug resistance phenotype.


   INTRODUCTION
Top
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Ovarian cancer is currently the leading cause of death from gynecological malignancies in the United States; approximately 80% of patients succumb to the disease within 5 years of diagnosis. Paclitaxel, originally isolated from Taxus brevafolia (pacific yew), is a microtubule stabilizing chemotherapeutic used to treat ovarian, breast, and non-small cell lung cancers. Unfortunately, paclitaxel therapy is often hindered by the development of drug resistance. The majority of in vitro drug resistance studies aimed at identifying mechanisms of paclitaxel resistance have compared drug naive cell lines to subclones demonstrating highly resistant phenotypes. Although this approach has identified several genetic changes in drug-resistant cell lines, it does not allow the identification of the earliest genetic changes underlying the drug resistance phenotype. To improve therapeutic strategies for women with ovarian cancer, it is necessary to distinguish early from late genetic changes in evolving paclitaxel resistance.

cDNA array analysis is an efficient technology that allows a global view of gene expression. Combined with mathematical models for pattern recognition and similarity clustering, this global expression can be organized into a manageable dataset (1) . A SOM,3 first described by Kohonen (2) , is an unsupervised learning algorithm useful in pattern recognition, which can describe changes over time in a single sample or related samples (3) . SOMs do not require a learning or test dataset, allowing pattern recognition without prior knowledge of the correct outcome (4) . cDNA array coupled with SOM analysis has recently been applied to various biological questions, including the yeast cell cycle, hematopoietic differentiation (5) , and inflammatory bowel disease (6) . Hierarchical clustering, either as an unsupervised or supervised learning algorithm, can identify patterns of similarity/difference between unrelated samples or individual genes (4) . Recent studies have used hierarchical clustering as well as other clustering analyses as a tool to genetically classify tumors (7 , 8) , predict chemosensitivity (9, 10, 11, 12) , and describe drug resistance (13, 14, 15) . A combination of SOM analysis and hierarchical clustering can identify patterns of change as a sample acquires a particular phenotype, in this case drug resistance, and can identify statistically significant clusterings of protein families, chromosomal locations, and molecular functions.

In this experiment, a paclitaxel-sensitive, ovarian carcinoma cell line, SKOV-3, was exposed to incrementally increasing concentrations of paclitaxel, resulting in the establishment of three paclitaxel-resistant sublines. The expression profile of each of the four cell lines (SKOV-3 and the three resistant lines) was determined by Affymetrix cDNA array of ~9600 known human genes. The expression profiles were then used to generate an SOM capable of differentiating patterns of change in gene expression across the paclitaxel-resistant sublines. The individual SOM partitions were then examined by hierarchical clustering to identify protein families significantly enriched in the given partition.


   MATERIALS AND METHODS
Top
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Cell Line Generation and Maintenance.
The SKOV-3 cell line was obtained from the American Type Culture Collection (Manassas, VA). SKOV-3 was exposed to incrementally increasing paclitaxel concentrations to generate three SKOV-3 sublines with varying degrees of resistance to paclitaxel. In an attempt to limit the founder effects associated with cloning, resistant cell lines are not strictly clonal; all surviving colonies were pooled after paclitaxel selection to create the respective resistant cell line. Cell lines were maintained in RPMI 1640 supplemented with 10% fetal bovine serum, 100 units/ml penicillin, and 100 µg/ml streptomycin (all reagents purchased from Life Technologies, Inc., Grand Island, NY). Resistant sublines were continuously cultured in paclitaxel, which was purchased from a commercial source, to ensure the drug-resistant phenotype.

RNA Extraction.
Total RNA was isolated from SKOV-3, SKOV-30.003TR, SKOV-30.03TR, and SKOV-30.3TR using Trizol Reagent (Life Technologies, Inc.) according to the manufacturer’s instructions. RNA was isolated from each cell line at three distinct time points identified by different passage numbers to account for and eliminate biological noise, as well as limiting noise because of different passage number and length. RNA quality was determined via ethidium bromide staining post agarose/formaldehyde gel electrophoresis.

High-Density Oligonucleotide Array Analysis.
Total RNA was processed and hybridized to Affymetrix Genechip HG-U95Av2 arrays (Affymetrix, Santa Clara, CA) by the Gene Array Technology Center at Partners Healthcare (Brigham and Women’s Hospital, Boston, MA) according to the manufacturer’s protocol. RNAs arrayed in triplicate were SKOV-3, SKOV-30.003TR, SKOV-30.03TR, and SKOV-30.3TR. Overall intensity of each individual array was scaled to 1500 to facilitate comparisons between arrays. Each array contained 12,386 probes corresponding to ~9,600 known human genes. Affymetrix Microarray Suite 5.0 was used to generate a signal intensity for each probe. Triplicate signal intensities were then averaged to generate the ASI for each probe. R2 values were calculated by Microsoft Excel, and Pearson Correlation Curves were graphed by Affymetrix Data Mining Tool 3.0 (Affymetrix).

SOM Generation.
SOM analysis was performed using Affymetrix Data Mining Tool 3.0 (Affymetrix). For each probe, an expression vector was defined by four ASIs obtained from the Microarray Suite 5.0 analysis, one/cell line: average SKOV-3, SKOV-30.003TR, SKOV-30.03TR, and SKOV-30.3TR. Therefore, each expression vector represents the temporal change in expression of a particular transcript as SKOV-3 develops a paclitaxel resistance phenotype. The 12,386 expression vectors were filtered for a fold change in expression greater than three and a variation of expression > 100. The 1319 expression vectors, which passed filtering, were normalized with a mean of zero and a variance of one. A four row by five column SOM was generated by 100 epochs with initial learning rate of 0.1, final learning rate of 0.005, initial neighborhood size of 5, and final neighborhood size of 0.2.

DNA Chip Analysis.
Hierarchical clustering of the SOM partitions was performed using DNA-Chip Analyzer software (D-chip; Harvard School of Public Health, Harvard University, Cambridge, MA; Ref. 16 ). Statistical significance was defined as a P < 0.005. Ps were calculated from the hypergeometric distribution based on gene ontology information recorded in the Gene Ontology Consortium (17) databases as of April 1, 2002.


   RESULTS
Top
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
cDNA Array Identifies an Increase in Variation of Gene Expression as SKOV-3 Acquires a Drug Resistance Phenotype.
SKOV-3, SKOV-30.003TR, SKOV-30.03TR, and SKOV-30.3TR triplicate arrays were hybridized, scanned, and assessed for quality. Individual signal intensities for each probe were computed and averaged across triplicates to obtain the ASI of each probe per cell line. Subsequently, the ASI was used in all additional analyses. As the SKOV-3 cell line progressed to a paclitaxel resistant state, an increase in the variation of gene expression between the resistant sublines and the parental SKOV-3 occurred. This increase in variance can be visualized by graphing the ASI of all 12,386 probes in SKOV-3 versus the corresponding ASI in SKOV-30.003TR, SKOV-30.03TR, and SKOV-30.3TR, respectively, using Pearson Correlation Curves (Fig. 1)Citation . The associated R2 values reflect that 94.6% of the gene expression in SKOV-30.003TR is attributable to its parental cell line SKOV-3; likewise, 92.6% of gene expression in SKOV-30.03TR, and 91.3% of gene expression in SKOV-30.3TR is attributable to SKOV-3. Therefore, as the cell lines demonstrate an increasingly paclitaxel-resistant phenotype, their gene expression profiles increasingly differ from the sensitive paclitaxel SKOV-3 parental line, with as much as 9% of global gene expression in SKOV-30.3TR not attributable to SKOV-3. Although this increase in variance may be partially attributable to differences in culture length and passage number, the collection of individual RNA samples at three distinct time points/cell line and the subsequent averaging of signal intensities across triplicates increases the probability that this increase in variance is associated with the emerging paclitaxel-resistant phenotype. Although it is unrealistic to assume that all of the variance observed is indicative of transcriptional changes that directly induce a paclitaxel resistance phenotype, this analysis has identified these transcriptional changes as being associated with the emerging paclitaxel resistance phenotype.



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  		Fig. 1. Pearson correlation plots and associated R2 values demonstrate an increase in variation from parental SKOV-3, as sublines become increasingly paclitaxel resistant.

 
A SOM Algorithm Can Successfully Define an Evolution of Paclitaxel Resistance.
As the R2 values suggest, an evolution of paclitaxel resistance exists in the SKOV-3 sublines. To define this evolution, a 20-partition SOM was constructed (Fig. 2)Citation . The SOM distinguishes between early, sustained increases (cluster 20) in gene expression and early, unsustained increases (clusters 11 and 16) in gene expression. It distinguishes early from intermediate (cluster 15) from late (cluster 10) increases in gene expression. As important to the drug resistance phenotype are early decreases in gene expression, visualized in clusters 1, 2, and 3. In essence, the SOM arbitrarily defined 20 expression patterns (the number of partitions is user defined) within the 1319 expression profiles, which passed initial filtering.



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  		Fig. 2. A 20-partition SOM representing an evolution of paclitaxel resistance. A total of 1319 expression profiles of SKOV-3, SKOV-30.003TR, SKOV-30.03TR, and SKOV-30.3TR passed a 3-fold change-in-expression filter and were clustered into 20 patterns. The number in parentheses after the pattern number is the number of probes partitioned into the cluster.

 
A Literature Review Identifying Genes Known to be Involved in Ovarian Cancer and/or Drug Resistance Validates the Generated SOM.
Genes previously identified as being involved in ovarian cancer and/or drug resistance is listed in Table 1Citation . The genes were located within the SOM or identified as having been filtered from the analysis. As expected, MDR-1 (18) transcripts passed filtering and were partitioned to cluster 15 (MDR-1 was represented by three independent probes on each array). In contrast, MRP-1, which is implicated in doxorubicin, daunorubicin, and vincristine resistance but not associated with paclitaxel resistance (19) , has been filtered from the analysis. Interestingly, the balance of apoptotic molecules has been recently implicated in the drug resistance phenotype (20, 21, 22) . This analysis demonstrates that as SKOV-3 acquires a paclitaxel resistance phenotype, the expression level of antiapoptotic Bcl-XL (23 , 24) is unchanged. However, the expression level of the apoptosis regulator of Bcl-XL (BAK, a proapoptotic molecule, which interacts with Bcl-XL; Ref. 25 ) decreases with progressive paclitaxel resistance. This ratio of anti- to proapoptotic molecules favoring survival may be responsible for overcoming a paclitaxel-induced death signal. Table 1Citation , listing additional genes with associated partitions, demonstrates that the SOM generated is consistent with previously reported molecular descriptions of paclitaxel resistance as seen in both cell lines and clinical samples. In addition, prior and ongoing northern studies in our laboratory of the SKOV-30.3TR cell line demonstrates overexpression of MDR-1 (26) , IL-6 (27) , IL-8 (27) , MAGE (28) , GAGE genes (28) , and human guanylate protein 1 (29) as compared with the SKOV-3 parental line (26, 27, 28, 29) . These genes are demonstrated to be overexpressed in the SOM and are represented in partitions 15, 5, 20, 20, 10, and 20, respectively. As such, this SOM may be useful in identifying novel genes associated with the early paclitaxel resistance phenotype.


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  		Table 1 Genes involved in ovarian cancer and drug resistance

 
Hierarchical Clustering Identifies Protein Families Significantly Enriched in Individual SOM Partitions.
Although individual changes in gene expression, as reported above, may be important in elucidating the molecular mechanisms of paclitaxel resistance, analysis of protein families and pathways provides a more realistic interpretation of genetic events. In an attempt to identify protein families that may be responsible for the paclitaxel resistance phenotype, hierarchical clustering of individual SOM partitions was performed. The authors acknowledge that although multiple SOM clusters represent potentially important early increases (clusters 11, 16, and 20) in gene expression, as well as potentially important early decreases (clusters 1, 2, and 3) in gene expression, for the purpose of illustration, SOM cluster 20 will represent early paclitaxel resistance, cluster 15 will represent intermediate paclitaxel resistance, and cluster 10 will represent late paclitaxel resistance. With this understanding, the early paclitaxel resistance phenotype is characterized by a sustained increase in expression of multiple inflammatory proteins (Table 2)Citation . Intermediate paclitaxel resistance is associated with a significant number of extracellular genes, transport genes, and G1-S transition genes (Table 3)Citation . Finally, late paclitaxel resistance is associated with an increase in expression of a number of tumor antigen, signal transducer, and peripheral plasma membrane genes (Table 4)Citation .


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  		Table 2 Protein families enriched in cluster 20 early paclitaxel resistance

 

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  		Table 3 Protein families enriched in cluster 15 intermediate paclitaxel resistance

 

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  		Table 4 Protein families enriched in cluster 10 late paclitaxel resistance

 
Additional Evaluation of Identified Genes and Protein Families.
Additional validation of the data presented in the analysis using northern, western, or quantitative reverse transcription-PCR would be useful to verify the integrity of the array data but will not prove that the effects are related to paclitaxel resistance. Distinguishing between genetic effects associated with paclitaxel resistance and genes capable of inducing paclitaxel resistance is a much longer and detailed process. Our laboratory is currently evaluating several candidate genes and gene families implicated in this analysis. For example, several cancer testes antigens associated with paclitaxel resistance in various resistant cell lines are also capable of directly inducing paclitaxel resistance upon transfection into the ovarian cancer cell line OVCAR 8. More comprehensive analysis of the functional changes associated with the genes identified in this analysis is outside of the scope of this preliminary study.


   DISCUSSION
Top
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
Paclitaxel resistance is indeed a complex phenotype characterized by a continuum of changes in gene expression. This study describes the transcriptional changes that occur in the ovarian cancer cell line, SKOV-3, with a progressive paclitaxel resistance phenotype. SKOV-3 was cultured for 8 months in the presence of increasing concentrations of paclitaxel. At three time points, sublines were selected to represent early, intermediate, and late paclitaxel resistance. cDNA arrays, performed in triplicate for each cell line, defined a global expression pattern for each cell line. An evolution of paclitaxel resistance was defined by an SOM analysis distinguishing early, intermediate, and late genetic changes. Hierarchical clustering identified statistically significant clusterings of protein families within the patterns of evolution.

This analysis demonstrated a broad range of transcriptional changes that satisfied our selection criteria for inclusion in the SOM with ~9% of the transcripts having greater than a 2-fold increase or decrease in expression between the highest and lowest expressing SKOV-3 cell line. Although, the concept that hundreds of genes might be involved in the evolution of drug resistance is not proven or generally accepted, several points should be emphasized. First, reversal of single genetic abnormalities (such as MDR-1) has not been successful at restoring drug sensitivity in the clinic. Second, the pathways associated with many genes are complex and alterations in transcription and associated protein expression may affect many genes downstream of the primary event. Thus, it is likely that only a small proportion of the genes identified in this analysis specifically induce paclitaxel resistance. Finally, the study of nontransformed, nonmalignant organisms has demonstrated surprisingly broad range of transcriptional changes with acquired resistance. For example, Candida albicans made resistant to the antifungal fluconazole demonstrates altered expression of 301 of 5000 open-reading frames representing 6% of the transcripts under analysis (30) .

A comprehensive discussion of all identified genes, gene families, or even SOM clusters is not practical. Nevertheless, a selective overview of three SOM clusters illustrates the potential use of this technology. For example, early paclitaxel resistance as reported here is associated with a statistically significant increase in inflammatory proteins, namely IL-8 and Exodus 1, chemokines capable of attracting various lymphocytes (31 , 32) , N-formyl peptide receptor 1, a chemoattractant usually expressed on neutrophils (33) , and adenosine A2a receptor, a regulator of stress response (34) . The implication that chemokines and inflammatory molecules are involved in carcinogenesis and drug resistance is not novel, yet the significance of such expression has not been elucidated. Mantovani et al. (35) hypothesize that chemokines secreted from tumors recruit macrophages, which in turn promote tumor growth and progression. In fact, IL-8, as well as other chemokines and cytokines, is elevated in the serum of ovarian cancer patients (36) and its overexpression is correlated to disease aggressiveness (37) . In addition to being chemoattractant and angiogenic, IL-8 induces haptotatic migration and proliferation of keratinocytes and melanoma cells (38 , 39) . Although, IL-8 transfection into paclitaxel-sensitive U-2OS cells does not induce a paclitaxel resistance phenotype, it does promote cell proliferation (40) . Although Exodus 1/macrophage inflammatory protein 3{alpha} has not been implicated in the drug resistance phenotype, it has been shown to promote proliferation of the pancreatic cell line COLO-357 and to promote the migration of pancreatic cell line PANC-1 (41) . Exodus 1 is under transcriptional control of NF-{kappa}B (42) , which is activated by tumor necrosis factor {alpha} in response to numerous stimuli, including proinflammatory cytokines and chemotherapeutic agents (43) . N-Formyl peptide receptor 1 ligand binding has also been shown to activate NF-{kappa}B (44) and to induce IL-8 secretion (44) . The adenosine A2a receptor when bound by extracellular adenosine released from metabolically active cells is an anti-inflammatory mediator and immunosuppressor (45) , which is also responsible for vasodilation of surrounding blood vessels (46) .

Intermediate paclitaxel resistance is represented in cluster 15. Hierarchical clustering of cluster 15, containing 119 probes, identified an enrichment of G1-S transition genes. Control of the cell cycle is essential not only for normal development but often is necessary for effective chemotherapy. Many chemotherapeutics rely on cell cycle checkpoints to recognize irreparably damaged cellular DNA leading to apoptosis. Indeed, paclitaxel arrests cells in mitosis by hyperstabilizing microtubules and interrupting normal chromosomal segregation (47, 48, 49) . In cancers as in normal cells, this cell cycle block is thought to induce apoptosis. However, as previously stated, the balance of apoptotic molecules in the SKOV-3 paclitaxel resistant sublines may be abrogated because of down-regulation of BAK expression. A shift toward survival combined with an increase in G1-S transition genes may potentially overcome a paclitaxel-induced death signal, promote cell proliferation, and hence contribute to the drug resistance phenotype.

The G1-S transition genes implicated in intermediate, progressive paclitaxel resistance include the pro-S-phase molecules: activator of S-phase kinase; growth factor independent 1; and S-phase kinase-associated protein 2 (p45). Activator of S-phase kinase has been shown to be essential for S-phase entry (50) , whereas growth factor independent 1 is thought to regulate gene expression during S phase (51) . S-phase kinase-associated protein 2, previously described as a proto-oncogene (52) , associates with p21, p19, and proliferating cell nuclear antigen to form the CDK2/cyclin A kinase necessary for S-phase entry (53) .

For example, some of the late paclitaxel resistance is associated with overexpression of multiple peripheral plasma membrane genes, including glial cell line-derived neurotrophic factor receptor {alpha}2, interleukin 1 receptor-associated kinase 1, and guanine nucleotide binding protein {gamma}-11. Glial cell line-derived neurotrophic factor receptor {alpha}2 when bound by neutrin (a glial cell line-derived neurotrophic factor family member) stimulates autophosphorylation of its coreceptor RET (rearranged during transfection) proto-oncogene (54 , 55) . Constitutively active RET has been implicated in carcinogenesis because of germ-line mutations (multiple endocrine neoplasia types 2a and 2b; Ref. 56 ) and gene rearrangement (papillary carcinoma of the thyroid; 57 ). Interleukin 1 receptor-associated kinase 1 associates with the IL-1 receptor upon ligand binding and leads to NF-{kappa}B activation in response to inflammation and stress (58 , 59) . Guanine nucleotide binding protein {gamma}-11 belongs to a multigene family, which encodes the {gamma} subunit of heterotrimeric G proteins, which coupled with receptors, is responsible for transducing extracellular signals intercellularly (60) . However, late paclitaxel resistance as described here within may not accurately reflect clinical drug resistance. SKOV-30.3TR is 100-fold more resistant to paclitaxel than parental SKOV-3 (data not shown), whereas clinically lethal disease is often characterized by much less dramatic changes in relative resistance. The SOM has distinguished these late genetic changes from those changes most likely to be involved in the early paclitaxel resistance of SKOV-3.

It is important to emphasize that the above description of transcriptional changes represents a somewhat arbitrary and selective review of a small subset of genes identified by filtering of the array data and subsequent SOM analysis. Nevertheless, this limited analysis demonstrates that the drug resistance phenotype may be more complex than anticipated, supporting data, which demonstrates that reversal of MDR-1 does not completely restore chemotherapeutic efficacy in tumors known to express MDR-1 (61) . In addition, cell lines are not necessarily representative of clinical disease. Sawiris et al. (62) recently demonstrated via 516 gene Ovachip that human epithelial ovarian cancer has a distinct gene expression signature as compared with various cell lines. In fact, the ovarian and colon cell lines clustered together rather than with tumors from the same tissue of origin. This analysis of the evolution of paclitaxel resistance should be repeated in either human disease or an animal model to additionally elucidate the underlying mechanisms of clinical drug resistance.

In conclusion, array technology combined with mathematical algorithms for pattern recognition and similarity clustering may be useful in defining complex phenotypes. Paclitaxel resistance is not explainable by a single genetic event; rather, it is the accumulation of events promoting cell survival and proliferation. This evolution of paclitaxel resistance describes 1000 changes in gene expression as SKOV-3 acquires a paclitaxel resistance phenotype. The SOM combined with hierarchical clustering has identified inflammatory proteins, G1-S transition proteins, and peripheral plasma membrane proteins as potentially important in different stages of paclitaxel resistance. Progress in this field will require the coupling of knowledge gained from such transcriptional analysis with functional techniques, which allow the analysis of polygenetic effects on cellular phenotype.


   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 This work is supported by NIH Grant CA 89150 and a grant from the Massachusetts Department of Public Health. This work was presented, in part, at the annual meeting of the American Association for Cancer Research, San Francisco, CA, April 2002. Back

2 To whom requests for reprints should be addressed, at Massachusetts General Hospital, Cox 640 Department of Hematology/Oncology, Boston, MA 02114. Phone: (617) 724-3123; Fax: (617) 724-3166; E-mail: mseiden{at}partners.org Back

3 The abbreviations used are: SOM, self-organizing map; ASI, average signal intensity; MDR-1, multidrug resistance gene 1; MRP, multidrug resistance-associated protein; IL, interleukin; NF-{kappa}B, nuclear factor {kappa}B. Back

Received 7/26/02. Accepted 3/ 4/03.


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 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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