Published in:
Open Access 01-06-2009 | Research article
Prediction of paclitaxel sensitivity by CDK1 and CDK2 activity in human breast cancer cells
Published in: Breast Cancer Research | Issue 1/2009
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Introduction
Paclitaxel is used widely in the treatment of breast cancer. Not all tumors respond to this drug, however, and the characteristics that distinguish resistant tumors from sensitive tumors are not well defined. Activation of the spindle assembly checkpoint is required for paclitaxel-induced cell death. We hypothesized that cyclin-dependent kinase (CDK) 1 activity and CDK2 activity in cancer cells, which reflect the activation state of the spindle assembly checkpoint and the growth state, respectively, predict sensitivity to paclitaxel.
Methods
Cell viability assays and DNA and chromatin morphology analyses were performed in human breast cancer cell lines to evaluate sensitivity to paclitaxel and the cell cycle response to paclitaxel. We then examined the specific activities of CDK1 and CDK2 in these cell lines and in xenograft models of human breast cancer before and after paclitaxel treatment. Protein expression and kinase activity of CDKs and cyclins were analyzed using a newly developed assay system.
Results
In the cell lines, biological response to paclitaxel in vitro did not accurately predict sensitivity to paclitaxel in vivo. Among the breast cancer xenograft tumors, however, tumors with significantly increased CDK1 specific activity after paclitaxel treatment were sensitive to paclitaxel in vivo, whereas tumors without such an increase were resistant to paclitaxel in vivo. Baseline CDK2 specific activity was higher in tumors that were sensitive to paclitaxel than in tumors that were resistant to paclitaxel.
Conclusions
The change in CDK1 specific activity of xenograft tumors after paclitaxel treatment and the CDK2 specific activity before paclitaxel treatment are both associated with the drug sensitivity in vivo. Analysis of cyclin-dependent kinase activity in the clinical setting could be a powerful approach for predicting paclitaxel sensitivity.