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Review | Open Access

Current Advances in Preclinical Patient-Derived Cultivation Models for Individualized Drug Response Prediction in Pancreatic Cancer

Benjamin Heckelmann#Jannis Duhn#Rüdiger Braun( )
Department of Surgery, University Medical Center Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, Lübeck, Germany

#These authors contributed equally to this work

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Abstract

Pancreatic ductal adenocarcinoma (PDAC) is currently the third leading cancer-related cause of death worldwide and is forecasted to become the second leading cause in the United States by 2030. Despite the development of multimodal treatment regimens, 5-year overall survival remained as low as 12%. Several efforts have been made to account for different aspects of heterogeneous tumor biology in PDAC, aiming to enable treatment stratification of defined subtypes. Besides targeting specific mutations, the definition of molecular (transcriptional) subtypes has gained substantial interest regarding response prediction and treatment stratification. Despite numerous advances in the field of genomic, transcriptomic, and proteomic characterization, the identified biomarkers do not yet facilitate predicting treatment response sufficiently in patients in vivo. Considering the growing evidence on the impact of the tumor microenvironment (TME) and intratumoral heterogeneity (ITH) on treatment resistance, there is an unmet clinical need for preclinical cultivation models that allow for predicting treatment response based on individual biological criteria. This review discusses the current advances in such in vivo (patient-derived xenografts) and ex vivo (organoids, organotypic slice cultures, cancer-on-chip) models for treatment response prediction and stratification in PDAC, and their potential implications in clinical translation.

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Oncology Research
Article number: 1

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Cite this article:
Heckelmann B, Duhn J, Braun R. Current Advances in Preclinical Patient-Derived Cultivation Models for Individualized Drug Response Prediction in Pancreatic Cancer. Oncology Research, 2026, 34(5): 1. https://doi.org/10.32604/or.2026.075028

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Received: 23 October 2025
Accepted: 30 January 2026
Published: 22 April 2026
© The Author 2026.

This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.