Fundación MEDINA, Centro de Excelencia en Investigación de Medicamentos Innovadores en Andalucía, Granada, Spain.
Medical Oncology Unit, University Hospital of Jaén, Spain.
Mol Oncol. 2022 Jul;16(14):2658-2671. doi: 10.1002/1878-0261.13216. Epub 2022 Apr 14.
Neoadjuvant chemotherapy (NACT) outcomes vary according to breast cancer (BC) subtype. Since pathologic complete response is one of the most important target endpoints of NACT, further investigation of NACT outcomes in BC is crucial. Thus, identifying sensitive and specific predictors of treatment response for each phenotype would enable early detection of chemoresistance and residual disease, decreasing exposures to ineffective therapies and enhancing overall survival rates. We used liquid chromatography-high-resolution mass spectrometry (LC-HRMS)-based untargeted metabolomics to detect molecular changes in plasma of three different BC subtypes following the same NACT regimen, with the aim of searching for potential predictors of response. The metabolomics data set was analyzed by combining univariate and multivariate statistical strategies. By using ANOVA-simultaneous component analysis (ASCA), we were able to determine the prognostic value of potential biomarker candidates of response to NACT in the triple-negative (TN) subtype. Higher concentrations of docosahexaenoic acid and secondary bile acids were found at basal and presurgery samples, respectively, in the responders group. In addition, the glycohyocholic and glycodeoxycholic acids were able to classify TN patients according to response to treatment and overall survival with an area under the curve model > 0.77. In relation to luminal B (LB) and HER2+ subjects, it should be noted that significant differences were related to time and individual factors. Specifically, tryptophan was identified to be decreased over time in HER2+ patients, whereas LysoPE (22:6) appeared to be increased, but could not be associated with response to NACT. Therefore, the combination of untargeted-based metabolomics along with longitudinal statistical approaches may represent a very useful tool for the improvement of treatment and in administering a more personalized BC follow-up in the clinical practice.
新辅助化疗（NACT）的结果因乳腺癌（BC）亚型而异。由于病理完全缓解是 NACT 最重要的目标终点之一，因此进一步研究 BC 中的 NACT 结果至关重要。因此，确定每种表型对治疗反应的敏感和特异性预测因子将能够早期发现化学耐药性和残留疾病，减少对无效治疗的暴露并提高总体生存率。我们使用基于液相色谱-高分辨率质谱（LC-HRMS）的非靶向代谢组学来检测三种不同 BC 亚型在接受相同 NACT 方案治疗后的血浆中的分子变化，目的是寻找潜在的反应预测因子。通过结合单变量和多变量统计策略对代谢组学数据集进行分析。通过使用方差分析-同时成分分析（ASCA），我们能够确定三阴性（TN）亚型中 NACT 反应潜在生物标志物候选物的预后价值。在反应者组中，分别在基础和术前样本中发现二十二碳六烯酸和次级胆汁酸的浓度较高。此外，甘氨胆酸和甘脱氧胆酸能够根据治疗反应和总生存曲线模型>0.77 将 TN 患者分类。关于管腔 B（LB）和 HER2+患者，应该注意到，显著差异与时间和个体因素有关。具体来说，在 HER2+患者中发现色氨酸随时间减少，而 LysoPE（22:6）似乎增加，但不能与 NACT 的反应相关联。因此，非靶向代谢组学与纵向统计方法的结合可能代表一种非常有用的工具，可用于改善治疗效果并在临床实践中对 BC 进行更个性化的随访。
