Marie-Christine Etienne-Grimaldi

Objectives: To investigate the relationship between uracilemia (U) and dihydropyrimidine dehydrogenase (DPD) activity in peripheral blood mononuclear cells (PBMC) and whether they are influenced by renal or hepatic impairment. Methods: This retrospective study included 176 cancer patients with pre-treatment U (UPLC-MSMS assay) and PBMC-DPD (radioenzymatic assay) analyzed the same day (routine phenotyping). Blood renal (creatinine, BUN) and hepatic (ALT, AST, GGT, ALP, albumin, bilirubin) work-up was performed within 15 days before or up to 4 days after DPD phenotyping. Biochemical markers were categorized according to CTCAEv5.0 grade (G). Glomerular filtration rate (eGFR) was estimated (CKD-EPI and EKFC). Non-parametric statistical tests were used. Results: Prevalence of partial deficiency was 3.4 % based on PBMC-DPD (i.e. ≤100 pmol/min/mg) and 6.3 % based on U (i.e. ≥16 μg/L). No complete deficiency was observed. Fifteen patients out of 176 (8.5 %) exhibited discordant DPD status between PBMC activity and U. The correlation between PBMC-DPD and U was significant but weak (r= −0.309, p1-UNL, p=0.009), GGT (G0 vs. G1 vs. G2 vs. G3, p<0.001), AST (G0 vs. G≥1, p=0.015), or with hypoalbuminemia (G0 vs. G ≥ 1, p=0.045). Categorized creatinine or eGFR did not influence U. Conclusions: It remains unclear whether renal and/or hepatic impairment acts as a confounding factor affecting the accuracy of uracilemia testing, or whether truly impacts DPD activity, suggesting caution in U interpretation.

This ABIGENE pharmacokinetic (PK) study sought mainly to characterize the unchanged drug PK during long-term abiraterone acetate (AA) administration in advanced prostate cancer patients (81 patients). It was observed that individual AA concentrations remained constant over treatment time, with no noticeable changes during repeated long-term drug administration for up to 120 days. There was no correlation between AA concentrations and survival outcomes. However, a significant association between higher AA concentrations and better clinical benefit was observed (p = 0.041). The safety data did not correlate with the AA PK data. A significant positive correlation (r = 0.40, p < 0.001) was observed between mean AA concentration and patient age: the older the patient, the higher the AA concentration. Patient age was found to impact steady-state AA concentration: the older the patient, the higher the mean AA concentration. Altogether, these data may help to guide future research and clinical trials in order to maximize the benefits of AA metastatic castration-resistant prostate cancer patients.

Abiraterone acetate (AA) is the first-in-class of drugs belonging to the second-generation of agents inhibiting androgen neosynthesis in advanced prostate cancer. A cumulative experience attests that germinal gene polymorphisms may play a role in the prediction of anticancer agent pharmacodynamics variability. In the present prospective, multicentric study, gene polymorphisms of CYP17A1 (AA direct target) and the androgen transporter genes SLCO2B1 and SLCO1B3 (potential modulators of AA activity) were confronted with AA pharmacodynamics (treatment response and toxicity) in a group of 137 advanced prostate cancer patients treated in the first line by AA. The median follow-up was 56.3 months (95% CI [52.5–61]). From multivariate analysis, rs2486758 C/C (CYP17A1) and PSA (≥10 ng/mL) were associated with a shorter 3-year biological PFS (HR = 4.05, IC95% [1.46–11.22]; p = 0.007 and HR = 2.08, IC95% [1.31–3.30]; p = 0.002, respectively). From a multivariate analysis, the rs743572 (CYP17A1) and performance status were independently associated with significant toxicity (OR = 3.78 (IC95% [1.42–9.75]; p = 0.006 and OR = 4.54; IC95% [1.46–13.61]; p = 0.007, respectively). Host genome characteristics may help to predict AA treatment efficacy and identify patients at risk for toxicity.

The prospective multicenter COMET trial followed a cohort of 306 consecutive metastatic breast cancer patients receiving bevacizumab and paclitaxel as first-line chemotherapy. This study was intended to identify and validate reliable biomarkers to better predict bevacizumab treatment outcomes and allow for a more personalized use of this antiangiogenic agent. To that end, we aimed to establish risk scores for survival prognosis dichotomization based on classic clinico-pathological criteria combined or not with single nucleotide polymorphisms (SNPs). The genomic DNA of 306 patients was extracted and a panel of 13 SNPs, covering seven genes previously documented to be potentially involved in drug response, were analyzed by means of high-throughput genotyping. In receiver operating characteristic (ROC) analyses, the hazard model based on a triple-negative cancer phenotype variable, combined with specific SNPs in VEGFA (rs833061), VEGFR1 (rs9582036) and VEGFR2 (rs1870377), had the highest predictive value. The overall survival hazard ratio of patients assigned to the poor prognosis group based on this model was 3.21 (95% CI (2.33–4.42); p < 0.001). We propose that combining this pharmacogenetic approach with classical clinico-pathological characteristics could markedly improve clinical decision-making for breast cancer patients receiving bevacizumab-based therapy.