Integrated clinical-bio logical infrastructure for precision oncology in pancreatic adenocarcinoma – experience with REDCap implementation in an academic setting

Czech version

Authors: P. Moravčík ¹; P. Weselá ^2,3; V. Kocandová ³; I. Aćimović ²; J. Hlavsa ¹; V. Procházka ¹; J. Vlažný ⁴; R. Bárta ⁵; Z. Kala ¹; P. Vaňhara ^2,6; M. Eid ^7,8
Authors‘ workplace: Chirurgická klinika LF MU a FN Brno ¹; Ústav histologie a embryologie LF MU ²; Oddělení biostatistiky, FN u sv. Anny v Brně ³; Ústav patologie LF MU a FN Brno ⁴; Klinika radiologie a nukleární medicíny LF MU a FN Brno ⁵; Centrum mezinárodního klinického výzkumu, FN u sv. Anny v Brně ⁶; Centrum precizní medicíny, FN Brno ⁷; Interní hematologická a onkologická klinika LF MU a FN Brno ⁸
Published in: Klin Onkol 2025; 38(6): 464-471
Category: Original Articles
doi: https://doi.org/10.48095/ccko2025464

Overview

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive solid malignancies, characterized by poor five-year survival and limited options for early detection. The development of precision oncology and personalized treatment requires structured and longitudinal collection of clinical and biological data. For this purpose, the REDCap web-based data management platform was utilized. Materials and methods: A project initiated by the Faculty of Medicine, Masaryk University, and University Hospital Brno aims to establish an integrated clinico-biological ecosystem for patients with PDAC. Data are managed within the REDCap system and include demographic characteristics, clinical features, treatment courses, molecular-genetic results, and survival outcomes. The pilot biobank stores patient-derived samples for the development of 3D models (organoids and spheroids). Data are analyzed using machine learning and artificial intelligence methods. Results: A structured database linking clinical data with biological materials has been established. To date, detailed longitudinal data have been collected from 117 patients with pancreatic (predominantly PDAC) and extrahepatic biliary tract tumors. The database records diagnostic and therapeutic procedures as well as molecular-genetic profiling data and their relationship to treatment response. It serves as a foundation for the development of predictive models and biomarker validation. Discussion: The project demonstrates the feasibility of comprehensive data collection within a university hospital setting. A major benefit is the ability to monitor treatment trajectories and implement precision oncology principles in clinical practice. Challenges include capacity and logistical demands, as well as the need for harmonization of input data. The initiative has the potential to expand into a national research infrastructure for PDAC. Conclusion: The established infrastructure represents a foundation for a data-driven approach to PDAC management. By integrating clinical data and biological models, it contributes to the advancement of personalized care and provides a platform for research and decision support in oncology.

Keywords:

precision medicine – pancreatic ductal adenocarcinoma – database – biobank

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