Gene Expression Profile Analysis Of Paclitaxel-Induced Changes In The Mda-Mb231 Human Breast Cancer Xenograft Model.

3196 Background: Paclitaxel (Taxol) is a microtubule binding agent routinely used in cancer treatment. Paclitaxel has been shown to be efficacious in patients with advanced and metastatic breast cancer, as well as in the adjuvant setting in early breast cancer. Numerous preclinical studies using various cancer cell lines have contributed to identify potential mechanisms of action of paclitaxel. However, little data are available on the molecular effects of paclitaxel in-vivo. Methods: We conducted cDNA micro-array and antitumor activity analysis to investigate molecular alterations and tumor growth inhibition induced by paclitaxel exposure in the MDA-MB231 mouse xenograft model. Tumor cells (2 x 107 cells/mouse) were subcutaneously implanted into nude mice. Mice were ranked according to tumor volume and randomized to receive either 15 mg/kg paclitaxel or vehicle from day 1 to 5 following randomization. Animals were sacrified and tumor and healthy mouse subcutaneous tissues harvested 6 hr following dosing on days 2 and 5, and immediately frozen in liquid nitrogen. MDA-MB231 cells exposed for 24 hr to 100 nM paclitaxel or vehicle in-vitro were also obtained in parallel experiments. Total RNAs were extracted and analyzed on cDNA microarrays containing ca. 9000 genes. Results: Kinetic analysis of paclitaxel-treated tumor tissues revealed strong in-vivo antitumor activity (T/C = 6.5%) at relevant pharmacologic doses and transcriptional alterations involving various intracellular pathways or systems, including apoptosis, mitotic regulation, cell cycle control, microtubule regulation, and cytoskeletal remodeling. Several differences were identified in paclitaxel-treated tumor tissues compared to drug-exposed cultured cells, and may explain discordant phenotypes of response between in-vitro and in-vivo evaluation. Conclusions: Gene expression profiling of drug effect in-vivo may improve preclinical assessment of anti-cancer compounds. In addition, such an approach may identify potential surrogate markers of drug effects that can be monitored in the clinical setting to predict clinical activity. Author Disclosure Employment or Leadership Consultant or Advisory Stock Ownership Honoraria Research Funding Expert Testimony Other Remuneration Ipsogen SAS; Oncodesign