Patient-Derived 3D-Bioprinted Models of Pancreatic Cancer: Toward Personalized Therapy and Overcoming Tumor Microenvironment Challenges

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive and treatment-resistant solid tumors, mainly due to its complex tumor microenvironment (TME). Characterized by dense desmoplastic stroma, immune suppression, and metabolic rewiring, the TME impairs drug delivery and eventually leads to therapeutic failure. Conventional models, such as two-dimensional (2D) cultures and mouse models, fail to recapitulate the cellular and mechanical intricacies of PDAC, limiting their translational relevance.

Three-dimensional (3D) models have emerged as promising tools to better simulate tumor biology. Among them, 3D-bioprinting techniques enable the precise spatial organization of cancer, stromal, and immune cells within tailored bioinks, supporting physiologically relevant architectures and dynamic microenvironmental interactions. These platforms allow controlled incorporation of extracellular matrix components, tunable stiffness, and perfusable vasculature, improving model fidelity and enabling real-time assessment of drug penetration, immune infiltration, and therapy resistance.

This review provides a comprehensive overview of current 3D PDAC modeling strategies, emphasizing patient-derived 3D-bioprinted models. We explore the key roles of bioink composition, extracellular matrix (ECM) stiffness, perfusion capacity, and immuno-compatibility in shaping the fidelity and utility of these models. Furthermore, we compare the structural complexity, scalability, drug-screening capabilities, and applicability for personalized medicine of different 3D models. By highlighting advances in vascularization, immune co-culture, and biofabrication technologies, we underscore the emerging value of 3D-bioprinting as a transformative platform for preclinical PDAC research. Ultimately, 3D-bioprinting is an important step forward in bridging the gap between preclinical studies and clinical implementation, as it opens the door to more accurate, personalized, and efficient therapeutic approaches for PDAC.