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Systemic immunometabolic profiling classifies cisplatin sensitivity states using interpretable machine learning
Journal article   Open access   Peer reviewed

Systemic immunometabolic profiling classifies cisplatin sensitivity states using interpretable machine learning

Emily Y. Kim, Diane C. Lim, Yujie Wang, Edison Q. Kim, Chunjing Wu, Ankita Paul, Cheng-Bang Chen and Medhi Wangpaichitr
iScience, v 29(3), 115037
20 Mar 2026
DOI: https://doi.org/10.1016/j.isci.2026.115037
PMID: 41852739
Featured in Collection :   Drexel's Newest Publications
url
https://doi.org/10.1016/j.isci.2026.115037View
Published, Version of Record (VoR)CC BY V4.0,  Open

Abstract

Computing methodology Metabolomics Machine Learning
Cisplatin resistance limits the effectiveness of platinum-based chemotherapy for lung adenocarcinoma, yet practical systemic diagnostics for cisplatin sensitivity are lacking. We developed ImmunoMetabolic Profiling Analysis and Classification Tool (IMPACT), an interpretable machine learning pipeline that selects the best performing model and reduces it to a minimal, mechanistically informative feature set via recursive feature elimination. In a syngeneic orthotopic model, we quantified 25 serum amino acids and 16 immune cell populations across bone marrow, spleen, lung, and mediastinal lymph nodes to capture systemic immunometabolic states. IMPACT classified cisplatin-sensitive versus cisplatin-resistant tumors with high accuracy (AUC = 0.950), driven primarily by bone marrow MDSCs and serum glutamine. Using the same framework, we also classified Cancer (CS + CR) versus no cancer controls with high accuracy (AUC = 0.955), with lung MDSCs and phosphoserine among the top features. • Interpretable machine learning reveals coordinated metabolic and immune interactions • Systemic immunometabolic profiling classifies cisplatin-sensitive vs. resistant tumors • Systemic immunometabolic profiling classifies cancer from noncancer states

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