ArticleJournal of clinical laboratory analysis2024
Interpretable Machine Learning Algorithms Identify Inetetamab-Mediated Metabolic Signatures and Biomarkers in Treating Breast Cancer.
Article in Journal of clinical laboratory analysis, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Metabolomics in breast cancer: insights into treatment responses, disease progression, and prognostic assessment.Metabolomics : Official journal of the Metabolomic Society · 2026Review
- Identification of ribosomal stress related signature genes and immune microenvironment analysis in ovarian cancer based on multi-machine learning.Discover oncology · 2026Article
- Machine Learning-Driven Insights in Cancer Metabolomics: From Subtyping to Biomarker Discovery and Prognostic Modeling.Metabolites · 2025Review
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Authors and funding
7 authors.
Funding
Abstract
backgroundHER2-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.
methodsThis 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.
resultsOur 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.
conclusionsIn 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.
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