Document Type
Article
Publication Date
3-20-2026
Abstract
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.
Publication Title
iScience
Volume
29
Issue
3
First Page
115037
PubMed ID
41852739
Recommended Citation
Klonoski, Emily; Lim, Diane C; Wang, Yujie; Kim, Edison Q; Wu, Chunjing; Paul, Ankita; Chen, Cheng-Bang; and Wangpaichitr, Medhi, "Systemic immunometabolic profiling classifies cisplatin sensitivity states using interpretable machine learning." (2026). PCOM Scholarly Works. 2360.
https://digitalcommons.pcom.edu/scholarly_papers/2360
DOI: https://doi.org/10.1016/j.isci.2026.115037
Comments
This article was published in iScience, Volume 29, Issue 3.
The published version is available at https://doi.org/10.1016/j.isci.2026.115037.
Copyright © 2026 The Author(s). CC BY 4.0.