# Systemic immunometabolic profiling classifies cisplatin sensitivity states using interpretable machine learning

## TL;DR

A machine learning tool called IMPACT classifies cisplatin sensitivity in lung cancer by analyzing immune and metabolic factors across the body.

## Contribution

The study introduces IMPACT, an interpretable machine learning framework that identifies minimal, biologically meaningful features for classifying cisplatin sensitivity.

## Key findings

- IMPACT accurately distinguishes cisplatin-sensitive from cisplatin-resistant tumors using bone marrow MDSCs and serum glutamine.
- The same framework also effectively classifies cancer versus non-cancer states with lung MDSCs and phosphoserine as key features.
- Systemic immunometabolic profiling captures coordinated interactions between immune and metabolic states.

## 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

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

Metabolomics; Computing methodology; Machine learning