3 resultados para DOCETAXEL
em Duke University
Resumo:
Notch signaling is implicated in prostate cancer progression and docetaxel resistance. Cui and colleagues describe the additive efficacy and mechanisms of a γ-secretase inhibitor, PF-03084014, and docetaxel in preclinical models of prostate cancer, suggesting the need for further clinical development of Notch pathway modulators in men with metastatic prostate cancer.
Resumo:
BACKGROUND: To explore the activity of dasatinib alone and in combination with gemcitabine and docetaxel in uterine leiomyosarcoma (uLMS) cell lines, and determine if dasatinib inhibits the SRC pathway. METHODS: SK-UT-1 and SK-UT-1B uLMS cells were treated with gemcitabine, docetaxel and dasatinib individually and in combination. SRC and paxcillin protein expression were determined pre- and post-dasatinib treatment using Meso Scale Discovery (MSD) multi-array immunogenicity assay. Dose-response curves were constructed and the coefficient of drug interaction (CDI) and combination index (CI) for drug interaction calculated. RESULTS: Activated phosphorylated levels of SRC and paxillin were decreased after treatment with dasatinib in both cell lines (p < 0.001). The addition of a minimally active concentration of dasatinib (IC25) decreased the IC50 of each cytotoxic agent by 2-4 fold. The combination of gemcitabine-docetaxel yielded a synergistic effect in SK-UT-1 (CI = 0.59) and an antagonistic effect in SK-UT-1B (CI = 1.36). Dasatinib combined with gemcitabine or docetaxel revealed a synergistic anti-tumor effect (CDI < 1) in both cell lines. The triple drug combination and sequencing revealed conflicting results with a synergistic effect in SK-UT-1B and antagonistic in SK-UT-1. CONCLUSION: Dasatinib inhibits the SRC pathway and yields a synergistic effect with the two-drug combination with either gemcitabine or docetaxel. The value of adding dasatinib to gemcitabine and docetaxel in a triple drug combination is uncertain, but may be beneficial in select uLMS cell lines. Based on our pre-clinical data and known activity of gemcitabine and docetaxel, further evaluation of dasatinib in combination with these agents for the treatment of uLMS is warranted.
Resumo:
BACKGROUND: A major challenge in oncology is the selection of the most effective chemotherapeutic agents for individual patients, while the administration of ineffective chemotherapy increases mortality and decreases quality of life in cancer patients. This emphasizes the need to evaluate every patient's probability of responding to each chemotherapeutic agent and limiting the agents used to those most likely to be effective. METHODS AND RESULTS: Using gene expression data on the NCI-60 and corresponding drug sensitivity, mRNA and microRNA profiles were developed representing sensitivity to individual chemotherapeutic agents. The mRNA signatures were tested in an independent cohort of 133 breast cancer patients treated with the TFAC (paclitaxel, 5-fluorouracil, adriamycin, and cyclophosphamide) chemotherapy regimen. To further dissect the biology of resistance, we applied signatures of oncogenic pathway activation and performed hierarchical clustering. We then used mRNA signatures of chemotherapy sensitivity to identify alternative therapeutics for patients resistant to TFAC. Profiles from mRNA and microRNA expression data represent distinct biologic mechanisms of resistance to common cytotoxic agents. The individual mRNA signatures were validated in an independent dataset of breast tumors (P = 0.002, NPV = 82%). When the accuracy of the signatures was analyzed based on molecular variables, the predictive ability was found to be greater in basal-like than non basal-like patients (P = 0.03 and P = 0.06). Samples from patients with co-activated Myc and E2F represented the cohort with the lowest percentage (8%) of responders. Using mRNA signatures of sensitivity to other cytotoxic agents, we predict that TFAC non-responders are more likely to be sensitive to docetaxel (P = 0.04), representing a viable alternative therapy. CONCLUSIONS: Our results suggest that the optimal strategy for chemotherapy sensitivity prediction integrates molecular variables such as ER and HER2 status with corresponding microRNA and mRNA expression profiles. Importantly, we also present evidence to support the concept that analysis of molecular variables can present a rational strategy to identifying alternative therapeutic opportunities.