Glycolytic heterogeneity drives metabolic-targeted therapy in PDAC

Methods: We employed spatial transcriptomics in six, ex vivo, primary human PDAC specimens, complemented by single-cell and bulk RNA sequencing, to map glycolytic heterogeneity in PDAC. Patient-derived cell models, representative of distinct glycolytic phenotypes, were used to assess metabolic and antiproliferative responses to glycolytic pathway inhibition. A comprehensive multi-omics strategy—including metabolomics, proteomics, and lipidomics—was integrated through a robust bioinformatics pipeline to dissect pathway-specific variations and integromics. Results: PDAC tumors displayed marked glycolytic heterogeneity, with distinct transcriptional profiles that preserved cell identity and spatial organization. These glycolytic patterns correlated with clinical outcomes, highlighting their potential prognostic value. Functional studies strengthen differential sensitivity to metabolic inhibitors in models with opposite glycolytic activity, demonstrating both the therapeutic relevance of PDAC glycolytic stratification and highlighting the potential of metabolic targeting. Conclusions: Our findings uncover a clinically relevant metabolic heterogeneity in PDAC, offering a novel stratification framework based on glycolytic profiling. This approach may allow the selection of patients for tailored metabolic therapies, advancing precision oncology in pancreatic cancer.