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

ABSTRACT:

Background

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.

Methods

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 t

SUBMITTER: Xie N 

PROVIDER: S-EPMC11632848 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature