Journal article
Systemic immunometabolic profiling classifies cisplatin sensitivity states using interpretable machine learning
iScience, v 29(3), 115037
20 Mar 2026
PMID: 41852739
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
• 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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Details
- Title
- Systemic immunometabolic profiling classifies cisplatin sensitivity states using interpretable machine learning
- Creators
- Emily Y. Kim - Research Services, Miami VA Healthcare System, Miami, FL 33125, USADiane C. Lim - University of MiamiYujie Wang - University of MiamiEdison Q. Kim - The Wistar InstituteChunjing Wu - Miami VA Healthcare SystemAnkita Paul - Drexel University, Electrical and Computer EngineeringCheng-Bang Chen - University of MiamiMedhi Wangpaichitr - University of Miami
- Publication Details
- iScience, v 29(3), 115037
- Publisher
- Elsevier Inc
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:001715982100001
- Scopus ID
- 2-s2.0-105031957076
- Other Identifier
- 991022170454904721
UN Sustainable Development Goals (SDGs)
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Source: SDGs in the Output
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Oncology