CYP3A4 is a key enzyme involved in the metabolism of numerous compounds, such as paclitaxel, and its activity shows an extensive inter-individual variation which can influence treatment response. The study's purpose was to investigate the potential predictive role of a CYP3A4 profile (CYP3A4*1B, rs2740574 and CYP3A4*22, rs35599367) in serous ovarian cancer patients treated with first-line chemotherapy (paclitaxel and cisplatin or carboplatin), after cytoreductive surgery. CYP3A4*1B and CYP3A4*22 genotypes were determined by Nested PCR-RFLP and Taqman® Allelic Discrimination, respectively. We observed that the mean survival rates were statistically different according the patients CYP3A4 genotypes. The group of patients carrying the CYP3A4*1B G allele present a decreased mean survival rate when compared with AA genotype patients (103.93 and 134.44 months, respectively, p = 0.010). This result is consistent after multivariate Cox regression analysis (HR, 2.15; 95% CI, 1.03-4.52; p = 0.043). The combination of CYP3A4*1B and CYP3A4*22 polymorphisms result in the definition of a CYP3A4 activity profile: the group of patients with a higher CYP3A4 activity profile had significantly diminished survival when compared with patients with a lower CYP3A4 activity profile (101.06 and 134.44 months, respectively, p = 0.012). Multivariate Cox regression analysis revealed a diminished overall survival time for patients with CYP3A4 high activity profile (HR, 2.29; 95% CI, 1.05-5.02; p = 0.038). The definition of a CYP3A4 activity profile resulted in the increase of prediction ability, using Harrels's concordance indexes (C-index from 0.617 to 0.626). To conclude, our results demonstrate an association between CYP3A4*1B and a diminished overall survival of patients with serous ovarian cancer. The definition of a CYP3A4 activity profile proved to be benefic and the CYP3A4 high activity profile was associated with a lower overall survival. We consider that the definition of a CYP3A4 activity profile might be useful as molecular marker for predicting the clinical outcome of serous ovarian cancer patients.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3731187PMC

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