Metabolic signatures and predictive biomarkers of liposomal doxorubicin-induced hypersensitivity in breast cancer: A pilot study.

[BACKGROUND] Liposomal doxorubicin hypersensitivity significantly affects breast cancer patients, with widely varying incidence rates (9 % to 46 %). Despite its efficacy, this condition poses a critical barrier to treatment due to the need for precise patient management and the absence of effective predictive biomarkers. This hypothesis-generating, exploratory study aimed to characterize plasma metabolic changes associated with hypersensitivity to liposomal doxorubicin in breast cancer patients, with the goal of identifying potential predictive biomarkers for future validation.

[METHODS] A retrospective metabolomic study was conducted on plasma samples from breast cancer patients treated with liposomal doxorubicin. Untargeted metabolomics was analyzed using LC-MS. Statistical methods, including Principal Component Analysis and Partial Least Squares Discriminant Analysis, were employed to identify significant metabolic changes, with a variable importance in projection score ≥1.0 and a p ≤ 0.05 as criteria for significance.

[RESULTS] Twelve breast cancer patients were included in this study (7 with immediate hypersensitivity). Differential metabolites revealed by the analysis included significant downregulation of 5,6-Dihydroxyeicosatrienoic acid (5,6-DHET) and kynurenic acid, along with marked upregulation of l-histidine. Pathway enrichment analysis highlighted dysregulation of key metabolic pathways, including the metabolism of phenylalanine, tyrosine, and β-alanine metabolism. Notably, the receiver operating characteristic curves demonstrated high diagnostic accuracy for several metabolites, including cytosine (AUC = 1.00), 5,6-DHET (AUC = 0.97), and l-histidine (AUC = 0.94), in predicting hypersensitivity.

[CONCLUSION] Liposomal doxorubicin-induced hypersensitivity involves a metabolic network characterized by impaired anti-inflammatory responses (5,6-DHET/kynurenic acid depletion), histamine pathway activation (L-histidine accumulation), and aromatic amino acid dysregulation. These findings establish a novel biomarker framework for predicting hypersensitivity risk and provide mechanistic insights for targeted interventions.