Machine learning-enabled spatial multi-omics uncovers lactate-driven targets and tumor microenvironmental reprogramming in cancer

Category	Detail
Condition	Lung adenocarcinoma (LUAD)
Key Mechanisms	Lactate accumulation drives metabolic reprogramming, angiogenesis, immune suppression, and microenvironment remodeling
Target Population	Patients with lung adenocarcinoma exhibiting variable tumor lactate levels
Care Setting	Oncology clinical and research settings focusing on tumor microenvironment and metabolic profiling

High lactate tumors show increased epithelial and fibroblast abundance; low lactate tumors enriched in T/NK cells and monocytes/macrophages
Spatial metabolomics reveals cell-type–restricted lactate distribution, with endothelial cells minimally accumulating lactate but showing angiogenic signatures in high-lactate tumors
Machine learning models identify endothelial and fibroblast programs as key determinants of high lactate states and poor clinical outcomes

Integrate single-cell transcriptomics, spatial transcriptomics, and spatial metabolomics for comprehensive tumor profiling
Assess tumor lactate levels to stratify metabolic heterogeneity and microenvironmental states

Target lactate-centered pathways to modulate angiogenesis and immune suppression in LUAD
Consider metabolic reprogramming as a therapeutic axis in high-lactate tumors

Use multi-omics and machine learning frameworks to monitor tumor microenvironment changes and therapeutic response
Evaluate endothelial and fibroblast activity as biomarkers for prognosis

High lactate microenvironments are associated with immune suppression and poor prognosis
Lactate-driven angiogenesis may contribute to tumor progression and therapy resistance

LUAD patients stratified by systemic and tumor lactate levels

Therapies targeting lactate metabolism and its downstream effects on endothelial and fibroblast cells may improve outcomes in high-lactate tumors

Employ integrative multi-omics approaches to capture spatial and cellular heterogeneity of lactate in tumors
Leverage machine learning models to identify key metabolic and cellular programs driving tumor progression
Focus on lactate-mediated immune and vascular interactions for developing precision therapies

Clinical Scorecard: Spatial Multi-Omics Enhanced by Machine Learning Reveals Lactate-Driven Therapeutic Targets and Reprogramming of the Tumor Microenvironment in Cancer