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Molecular description of evolving paclitaxel resistance in the SKOV-3 human ovarian carcinoma cell line
Diana E Lamendola1, Zhenfeng Duan, Rushdia Z Yusuf
1Division of Hematology/Oncology, Massachusetts General Hospital Boston 02114, USA.
Abstract:
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 micro M), intermediate (0.03 micro M), and late (0.3 micro M) paclitaxel resistance. RNA from these cell lines, SKOV-3(0.003TR), SKOV-3(0.03TR), and SKOV-3(0.3TR), 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 approximately 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 approximately 11,000 of approximately 12,000 expression patterns. The remaining approximately 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-3(0.003TR) as compared with parental SKOV-3 but demonstrates elevated expression in SKOV-3(0.03TR) and SKOV-3(0.3TR). In contrast, SOM analysis demonstrates early (SKOV-3(0.003TR)) 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.
Insights
This study investigated the evolution of paclitaxel resistance in ovarian cancer cells. Gene expression profiling revealed distinct transcriptional changes at early, intermediate, and late stages of resistance, identifying potential targets for overcoming drug resistance.
Area of Science:
- Gynecological Oncology
- Molecular Biology
- Genomics
Background:
- Ovarian cancer is a leading cause of cancer death in the US.
- Multidrug resistance limits the effectiveness of current therapies.
- Understanding the mechanisms of drug resistance is crucial for developing new treatments.
Purpose of the Study:
- To define the evolution of paclitaxel resistance in ovarian cancer.
- To identify gene expression patterns associated with progressive paclitaxel resistance.
- To explore candidate genes involved in early-stage drug resistance.
Main Methods:
- Developed three SKOV-3 ovarian cancer sublines with increasing paclitaxel resistance (early, intermediate, late).
- Analyzed RNA expression profiles using Affymetrix HG-U95Av2 cDNA arrays (approx. 9600 genes).
- Utilized self-organizing maps (SOM) for expression pattern analysis and identification of differentially expressed genes.
Main Results:
- SOM analysis successfully discriminated gene expression patterns across progressive paclitaxel resistance levels.
- Multidrug resistance 1 (MDR1) transcript expression increased at intermediate and late resistance stages.
- Early paclitaxel resistance was associated with transcriptional changes in genes related to cell growth, structure, signaling, and inflammation.
Conclusions:
- Array analysis combined with SOM effectively defines the evolution of paclitaxel resistance.
- This approach can identify candidate gene families involved in the early-drug resistance phenotype.
- Findings provide insights into the molecular mechanisms underlying ovarian cancer drug resistance.
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