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Glycolytic heterogeneity drives metabolic-targeted therapy in pancreatic ductal adenocarcinoma

U. Chianese,Chiara Papulino,Gerardo Saggese,Ahmad Ali,Marianna Ciotola,E. Lonardo,Mirko Cortese,G. Favale,A. Di Mauro,Danila La Gioia,Valentina Golino,E. Sommella,Pietro Campiglia,Renato Franco,F. Ciardiello,F. De Vita,V. Carafa,L. Altucci,R. Benedetti

Published 2026 in Signal Transduction and Targeted Therapy

ABSTRACT

Pancreatic ductal adenocarcinoma is traditionally characterized as a glycolytic tumor. However, the extent and clinical relevance of its metabolic heterogeneity remain poorly understood. In this study, we investigated whether glycolytic activity follows a consistent expression pattern across pancreatic ductal adenocarcinoma patients and explored how metabolic diversity influences therapeutic responses. Using spatial transcriptomics of ex vivo primary human pancreatic ductal adenocarcinoma specimens, along with single-cell and bulk RNA sequencing, we mapped glycolytic heterogeneity within the tumor microenvironment. Patient-derived cell models representing distinct glycolytic phenotypes were employed to assess metabolic profiles and responses to glycolytic pathway inhibition. A multiomics approach—including metabolomics, proteomics, and lipidomics—was integrated through a robust bioinformatics pipeline to identify pathway-specific variations. Our findings revealed pronounced glycolytic heterogeneity across pancreatic ductal adenocarcinoma tumors, with distinct transcriptional profiles that maintained cellular identity and spatial architecture. These glycolytic patterns are associated with clinical outcomes, suggesting their potential as prognostic indicators. Functional studies confirmed differential sensitivity to metabolic inhibitors in organoids and demonstrated their safety across models, supporting the therapeutic relevance of glycolytic stratification. Overall, this study reveals clinically significant metabolic heterogeneity in pancreatic ductal adenocarcinoma and proposes a glycolysis-based framework for patient stratification, which could guide personalized metabolic therapies and advance precision oncology in pancreatic cancer.

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