Targeted metabolomics reveals tricarboxylic acid cycle-related metabolites serve as predictive biomarkers for trastuzumab resistance in human epidermal growth factor receptor 2-positive breast cancer.

Trastuzumab, a monoclonal antibody against human epidermal growth factor receptor 2 (HER2), has been proven to be effective in enhancing overall survival in patients with HER2-positive breast cancer. However, some patients exhibit resistance to trastuzumab-based regimens, resulting in suboptimal clinical outcomes. This study aimed to investigate tricarboxylic acid (TCA) cycle-related metabolites as potential biomarkers for predicting trastuzumab resistance. An ultra-performance liquid chromatography-mass spectrometry (UPLC-MS/MS)-multiple reaction monitoring (MRM)-based targeted metabolomics method was performed to identify differences in the abundances of serum TCA cycle-related metabolites (citric acid, cis-aconitic acid, isocitric acid, α-ketoglutaric acid, succinic acid, fumaric acid, malic acid, and oxaloacetic acid) in healthy controls (HC) and patients with HER2-positive breast cancer. Least absolute shrinkage and selection operator based logistic regression (LASSO-LR) analysis were performed to construct a nomogram for predicting the incidence of trastuzumab resistance (TR). The predictive accuracy and clinical utility of the model were evaluated using receiver operating characteristic curve and decision curve analyses, respectively. In addition, the abundances and effects of selected metabolic biomarkers on trastuzumab resistance were validated in trastuzumab sensitivity (TS) and TR cell lines. TCA cycle-related metabolites were separated in a 4-min run, and the method was validated over a broad concentration range, revealing good linearity, accuracy, and precision. Differential analysis revealed consistent elevations in fumaric acid, malic acid, and succinic acid abundances in patients with HER2-positive breast cancer compared to HC and in the TR cell line compared to those in the TS cell line. Using the LASSO-LR model, four variables were found to be significant predictors of trastuzumab resistance and were included in the nomogram: fumaric acid, malic acid, clinical status and lymph node metastasis. A high-risk group susceptible to resistance to trastuzumab was identified based on the nomogram scores. In vitro assays indicated the accumulation of fumaric acid and malic acid effectively alleviated the proliferation inhibitory and inducing apoptosis effects of trastuzumab on TS cell line. Fumaric acid and malic acid were identified as biomarkers for predicting the incidence of TR, and high abundances of these metabolites significantly reduced the sensitivity of the TS cell line to trastuzumab.