Gal Jocelyn, Milano Gérard, Brest Patrick, Ebran Nathalie, Gilhodes Julia, Llorca Laurence, Dubot Coraline, Romieu Gilles, Desmoulins Isabelle, Brain Etienne, Goncalves Anthony, Ferrero Jean-Marc, Cottu Paul-Henri, Debled Marc, Tredan Olivier, Chamorey Emmanuel, Merlano Marco Carlo, Lemonnier Jérôme, Etienne-Grimaldi Marie-Christine, Pierga Jean-Yves
Epidemiology and Biostatistics Department, Centre Antoine Lacassagne, University Côte d'Azur, 06100 Nice, France.
Cancer Pharmacogenetics and Radiogenetics Unit (UPRC) 7497, Centre Antoine Lacassagne, University Côte d'Azur, 33 avenue de Valombrose, 06100 Nice, France.
Pharmaceuticals (Basel). 2020 Nov 23;13(11):414. doi: 10.3390/ph13110414.
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 (rs833061), (rs9582036) and (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); < 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.
前瞻性多中心COMET试验追踪了306例连续接受贝伐单抗和紫杉醇作为一线化疗的转移性乳腺癌患者队列。本研究旨在识别和验证可靠的生物标志物，以更好地预测贝伐单抗的治疗结果，并使这种抗血管生成药物的使用更加个体化。为此，我们旨在基于经典临床病理标准联合或不联合单核苷酸多态性（SNP）建立生存预后二分法的风险评分。提取了306例患者的基因组DNA，并通过高通量基因分型分析了一组13个SNP，这些SNP覆盖了先前记录的可能参与药物反应的7个基因。在受试者工作特征（ROC）分析中，基于三阴性癌症表型变量并结合 （rs833061）、 （rs9582036）和 （rs1870377）中的特定SNP的风险模型具有最高的预测价值。基于该模型被分配到预后不良组的患者的总生存风险比为3.21（95%CI（2.33 - 4.42）；P < 0.001）。我们建议将这种药物遗传学方法与经典临床病理特征相结合，可以显著改善接受基于贝伐单抗治疗的乳腺癌患者的临床决策。
