Medical Oncology Department, Centre Léon Bérard, Lyon, France.
Prevention and Public Health Department, Centre Léon Bérard, Lyon, France.
Clin Cancer Res. 2024 Oct 15;30(20):4654-4666. doi: 10.1158/1078-0432.CCR-24-0195.
Long-term treatment-related toxicities, such as neurologic and metabolic toxicities, are major issues in breast cancer. We investigated the interest of metabolomic profiling to predict toxicities.
Untargeted high-resolution metabolomic profiles of 992 patients with estrogen receptor (ER)+/HER2- breast cancer from the prospective CANTO cohort were acquired (n = 1935 metabolites). A residual-based modeling strategy with discovery and validation cohorts was used to benchmark machine learning algorithms, taking into account confounding variables.
Adaptive Least Absolute Shrinkage and Selection (adaptive LASSO) has a good predictive performance, has limited optimism bias, and allows the selection of metabolites of interest for future translational research. The addition of low-frequency metabolites and nonannotated metabolites increases the predictive power. Metabolomics adds extra performance to clinical variables to predict various neurologic and metabolic toxicity profiles.
Untargeted high-resolution metabolomics allows better toxicity prediction by considering environmental exposure, metabolites linked to microbiota, and low-frequency metabolites.
长期的治疗相关毒性，如神经毒性和代谢毒性，是乳腺癌的主要问题。我们研究了代谢组学分析预测毒性的意义。
从前瞻性 CANTO 队列中获得了 992 例雌激素受体（ER）+/HER2-乳腺癌患者的非靶向高分辨率代谢组学图谱（n=1935 种代谢物）。采用基于残差的建模策略，结合发现和验证队列，利用机器学习算法进行基准测试，同时考虑混杂变量。
自适应最小绝对收缩和选择（adaptive LASSO）具有良好的预测性能，具有有限的乐观偏差，并允许选择有前途的代谢物进行未来的转化研究。添加低频代谢物和未注释的代谢物可以提高预测能力。代谢组学通过考虑环境暴露、与微生物群相关的代谢物和低频代谢物，为临床变量提供了额外的性能，以预测各种神经毒性和代谢毒性特征。
非靶向高分辨率代谢组学通过考虑环境暴露、与微生物群相关的代谢物和低频代谢物，能够更好地预测毒性。
