Interpretable Machine Learning Algorithms Identify Inetetamab-Mediated Metabolic Signatures and Biomarkers in...

HER2-positive breast cancer (BC), a highly aggressive malignancy, has been treated with the targeted therapy inetetamab for metastatic cases. Inetetamab (Cipterbin) is a recently approved targeted therapy for HER2-positive metastatic BC, significantly prolonging patients' survival. Currently, there is no established biomarker to reliably predict or assess the therapeutic efficacy of inetetamab in BC patients.

This study harnesses the power of metabolomics and machine learning to uncover biomarkers for inetetamab in BC therapy. A total of 23 plasma samples from inetetamab-treated BC patients were collected and stratified into responders and nonresponders. Ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry was utilized to analyze the metabolites in blood samples. A combination of univariate and multivariate statistical analyses was employed to identify these metabolites, and their biological functions were then ascertained by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Finally, machine learning algorithms were employed to screen responsive biomarkers from all differentially expressed metabolites.

Our finding revealed 6889 unique metabolites that were detected. Pathways like retinol metabolism, fatty acid biosynthesis, and steroid hormone biosynthesis were enriched for differentially expressed metabolites. Notably, two key metabolites associated with inetetamab response in BC were identified: FAPy-adenine and 2-Pyrocatechuic acid. There was some negative correlation between progress-free survival (PFS) and their kurtosis content.

In summary, the identification of these two significant differential metabolites holds promise as potential biomarkers for evaluating and predicting inetetamab treatment outcomes in BC, ultimately contributing to the diagnosis of the disease and the discovery of prognostic markers.