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Introduction

The PAN-PROMISE symptom score is the first patient-reported outcome (PROM) in acute pancreatitis (AP), and it was developed and validated in a prospective cohort of patients to be used as an endpoint in research. The aim of this study was to assess the performance of the score in a large randomized controlled trial (RCT) and its association with well-established AP endpoints.

Aims & Methods

This is an ancillary study of the WATERFALL trial, where PAN-PROMISE was evaluated at baseline, 24, 48 and 72 hours. The study examined the association between PAN-PROMISE with stablished endpoints in AP: severity, pancreatic and/or peripancreatic fat necrosis (necrosis), infected necrosis, intensive care unit (ICU) admission, persistent organ failure (POF), prolonged hospital stay and mortality. Areas under the ROC curve (AUC) were calculated and used to compare baseline PAN-PROMISE and baseline Bedside Index for Severity in AP (BISAP) for the prediction of these endpoints.

Results

248 patients from the WATERFALL trial were included. A statistically significant association was found between PAN-PROMISE and severity, necrosis, POF, ICU admission, and prolonged hospital stay at all checkpoints (p<0.05). Higher scores were also significantly associated with infected pancreatic necrosis at 24, 48, and 72h and death at baseline and 24 hours. PAN-PROMISE baseline score had a higher AUC than BISAP for severity and necrosis, but the results were not statistically significant.

Conclusion

In the context of a RCT, the PAN-PROMISE score, a patient-centered measure, has been validated with established AP outcomes and as an endpoint for future clinical trials.

USEFULNESS OF PAN-PROMISE SYMPTOM SCORE AS AN ENDPOINT IN RANDOMIZED TRIALS IN ACUTE PANCREATITIS, A VALIDATION STUDY

Lucía Guilabert 1, Karina Cárdenas-Jaén 1, James L. Buxbaum 2, Ana Garcia Garcia de Paredes 3, Lorena Bernal 1, Jose Alberto Andreu Viseras 1, Sara Pérez-Brotons 1, Alicia Vaillo-Rocamora 1, Jesús Donate Ortega 3, Eduardo Tavío Hernández 3, Édgar E. Lozada Hernández 4, Diego Hinojosa Ugarte 4, Alba Lira Aguilar 5, Patricia Pedregal-Pascual 5, Rajiv Mehta 6, Dhvani D. Adhvaryu 6, Pablo Navarro Cortés 7, Isabel Pascual-Moreno 7, Claudia Sánchez-Marin 8, Marina Cobreros del Caz 8, Idaira Fernández Cabrera 9, Fernando Casals Seoane 10, Diego Casas Deza 11, Eugenia Lauret-Braña 12, Andrea García Gómez 13, Laura María Camacho-Montaño 14, David Ruiz-Clavijo Garcia 15, Belen GONZÁLEZ DE LA HIGUERA CARNICER 15, Federico Bolado Concejo 15, Rodrigo Jover 16, José J. Mira 17, Enrique de-Madaria 18

1 Dr. Balmis General University Hospital, ISABIAL, Alicante, Spain

2 University of Southern California, Los Angeles, United States

3 Hospital Universitario Ramon y Cajal, Madrid, Spain

4 Servicios de Salud del Instituto Mexicano del Seguro Social para el Bienestar (IMSS-BIENESTAR), León-Guanajuato, Mexico

5 Corporació Sanitària Parc Taulí, Sabadell, Spain

6 Surat Institute of Digestive Science (SIDS) Hospital and Research Centre, Surat, India

7 Hospital Clinico Universitario, Valencia, Spain

8 Marqués de Valdecilla University Hospital, Santander, Spain

9 Dr. Negrín Hospital, Las Palmas, Spain

10 Hospital Universitario de La Princesa, Madrid, Spain

11 Hospital Universitario Miguel Servet, Zaragoza, Spain

12 Central de Asturias University Hospital,Institute of Health Research of the Principality of Asturias ISPA, Oviedo, Spain

13 Lucus Augusti University Hospital, Lugo, Spain

14 Puerta del Mar University Hospital, Cadiz, Spain

15 Complejo Hospitalario Navarra, Pamplona, Spain

16 Dr. Balmis General University Hospital, ISABIAL, Alicante, Spain|||Miguel Hernández University, Elche, Spain

17 Miguel Hernández University of Elche, Elche, Spain|||Atenea Research Group, Foundation for the Promotion of Health and Biomedical Research of Valencia Region, FISABAO, San Juan, Spain

18 Miguel Hernández University, Elche, Spain|||Dr. Balmis General University Hospital, ISABIAL, Alicante, Spain

Conference

UEG Week Berlin 2025

Topics

Pancreas

Submission format

Abstract

Session

Acute pancreatitis: Complications and consequences (Posters)

Citation

United European Gastroenterology Journal 2025; 13 (Supplement 8)

Published

2025

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MACHINE LEARNING-DRIVEN PROGNOSTIC MODELING IN PANCREATIC DUCTAL ADENOCARCINOMA USING CLINICOPATHOLOGIC AND CAF EXPRESSION DATA

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Introduction

Pancreatic Ductal Adenocarcinoma (PDAC) remains one of the deadliest solid tumors, largely due to its late-stage diagnosis, limited therapeutic responsiveness, and poor overall prognosis. Despite ongoing advances in imaging techniques and molecular characterization, effective tools for early prognostic stratification are still lacking. Recent research suggests that integrating machine learning (ML) with clinicopathologic variables and tumor microenvironment components, particularly cancer-associated fibroblasts (CAFs, may significantly enhance predictive accuracy. Leveraging these computational methods could facilitate earlier intervention and guide personalized treatment decisions, offering a potential paradigm shift in PDAC management.

Aims & Methods

This study aims to develop and validate machine learning models that incorporate clinicopathologic parameters and cancer-associated fibroblast (CAF) expression profiles to enhance prognostic stratification in patients with PDAC. A retrospective analysis was conducted on a cohort of 76 patients diagnosed with PDAC through endoscopic ultrasound-guided fine-needle biopsy (EUS-FNB). Data collected included alcohol consumption, smoking status, type II diabetes, C-reactive protein (CRP), albumin, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), CRP/albumin ratio, carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA 19-9), CA 125, and expression levels of CAF markers S100A4 and alpha-smooth muscle actin (α-SMA). The primary endpoint was the classification of tumor stage into early (T1–T2) and advanced (T3–T4) categories. Four machine learning algorithms—Latent Dirichlet Allocation (LDA), Quadratic Discriminant Analysis (QDA), Logistic Regression (LR), and K-Nearest Neighbors (K-NN)—were applied and their performance evaluated using independent validation datasets.

Results

All four machine learning models demonstrated strong predictive performance, with accuracy scores ranging from 0.77 to 1.00. Quadratic Discriminant Analysis (QDA) emerged as the most accurate classifier. Latent Dirichlet Allocation (LDA) achieved 91% accuracy and identified the neutrophil-to-lymphocyte ratio (NLR) as a key feature associated with early-stage (T1–T2) disease. In contrast, CRP/albumin ratio and PLR were more predictive of advanced-stage (T3–T4) tumors. Logistic Regression (LR), with an 83% accuracy rate, also highlighted PLR, CRP/albumin, and CAF markers (S100A4 and α-SMA) as significant contributors to advanced staging. These findings underscore the potential of integrating clinicopathologic and stromal biomarkers into ML-driven prognostic models.

Conclusion

Integrating machine learning with clinicopathologic and cancer-associated fibroblast (CAF) expression data offers a promising strategy for improving prognostic assessment in PDAC. By accurately distinguishing early- from advanced-stage disease, these models can support more personalized risk stratification and treatment planning. The results underscore the value of AI-driven approaches in precision oncology, with potential to enhance clinical decision-making and ultimately improve outcomes for patients with pancreatic cancer.

MACHINE LEARNING-DRIVEN PROGNOSTIC MODELING IN PANCREATIC DUCTAL ADENOCARCINOMA USING CLINICOPATHOLOGIC AND CAF EXPRESSION DATA

Bogdan Ungureanu 1, Ioana Streata 2, Mirela Florescu 2, Cristian Lungulescu 2, ION ROGOVEANU 2, Daniel Pirici 2, Lucian Florescu 2, Adina Turcu-Stiolica 2

1 University of Medicine and Pharmacy of Craiova, Craiova, Romania

2 University of Medicine and Pharmacy of Craiova Romania, Craiova, Romania

Event

UEG Week Berlin 2025

Topics

Digestive Oncology Pancreas

Submission format

Abstract

Session

Prediction and prognosis of pre-malignant and malignant pancreatic lesions

Citation

United European Gastroenterology Journal 2025; 13 (Supplement 8)

Published

2025

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A fusion-based deep-learning algorithm predicting PDAC metastasis from primary tumour

Núria Malats 1

1 Spanish Cancer Research Cancer Center, Madrid - Genetic and Molecular Epidemiology Group, Spanish Ca, Madrid, Spain

Event

UEG Week Berlin 2025

Topics

Digestive Oncology Hepatobiliary Mechanisms & Personalised Medicine

Session

Predicting therapy response

Citation

United European Gastroenterology Journal 2025; 13 (Supplement 8)

Published

2025

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Prediction of Clinical Events in Patients with MASLD Based on Baseline and Serial Changes in MRE

Takashi Kobayashi 1

1 Yokohama City University Graduate School of Medicine, Yokohama, Japan

Event

UEG Week Berlin 2025

Topics

Hepatobiliary

Session

Rising Stars from Europe and Japan: Metabolic liver diseases

Citation

United European Gastroenterology Journal 2025; 13 (Supplement 8)

Published

2025

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Visceral thrombosis in pancreatic disorders: Incidence and treatment algorithms

Peter Hegyi 1

1 University of Pecs, Pecs, Hungary

Event

UEG Week Berlin 2025

Topics

Nurses Pancreas Radiology & Imaging

Session

Hot topics in European gastroenterology

Citation

United European Gastroenterology Journal 2025; 13 (Supplement 8)

Published

2025

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Introduction

Intraductal papillary mucinous neoplasms (IPMNs) are a common type of mucinous pancreatic cystic lesions and represent a precursor of pancreatic cancer. The WHO classification distinguishes three grades of IPMN — low-grade/high-grade intraepithelial neoplasia and IPMN-associated invasive carcinoma—with distinct clinical management. The correct estimation of IPMN biology in clinical practice is often challenging and relies particularly on the assessment of lesion morphology, as histological confirmation is seldom possible. Therefore, improved biomarkers are needed to guide personalized management of IPMN patients.
In our preliminary analysis of public RNA-seq datasets (healthy controls n=7; LGD IPMN n=6; HGD IPMN n=6; invasive IPMN n=3), DLGAP5 expression increased significantly with IPMN grade (ANOVA p=5.48×10⁻⁶). DLGAP5 is a microtubule-associated protein essential for mitosis and vesicular trafficking—processes implicated in pancreatic cancer progression.

Aims & Methods

We aimed to assess the utility of DLGAP5 as a marker of IPMN progression as well as identify potential surrogate cystic-fluid biomarkers. Because of the intracellular localization of DLGAP5, we did not expect it to be present in the cyst fluid. However, given the role of microtubules in protein secretion, we hypothesized that DLGAP5 regulates the secretion of distinct set of proteins, which are highly correlated with its expression.
Protein expression of DLGAP5 was evaluated by immunohistochemistry (Santa Cruz Biotechnology, sc-377044) on retrospective cohort of patients who underwent pancreatic resection for IPMN at our institution (n=45). Functional studies were conducted in vitro by modulating DLGAP5 expression in pancreatic cancer cell lines (PaTu 8902, MiaPaCa2) and IPMN precursor cell line (ASAN-PaCa). Mass spectrometry-based secretome analysis of IPMN precursor cells was performed to identify DLGAP5-associated secreted proteins with potential for clinical testing in cyst fluid.

Results

DLGAP5 staining density and intensity differed significantly among LGD, HGD, and carcinoma (Fisher–Freeman–Halton p<0.0001 for both). Post-hoc Fisher’s exact tests showed LGD vs HGD (density p<0.001; intensity p<0.001) and LGD vs carcinoma (density p<0.001; intensity p<0.001) to be significant; HGD vs carcinoma was not (density p=0.673; intensity p=0.414). Functional assays demonstrated that DLGAP5 knockdown in pancreatic cancer cells decreased proliferation, while its overexpression in IPMN precursor cells increased proliferation. Secretome profiling revealed eight proteins whose secretion was significantly increased in IPMN precursor cells overexpressing DLGAP5 compared to empty vector expressing cells. Among them, PRDX1, PRDX2, FABP5, and SCGB1D2 (p=0.03, 0.038, 0.035 and 0.009 respectively) have previously been identified in cyst fluid or serum of IPMN patients, indicating translational relevance.

Conclusion

In conclusion, we validated DLGAP5 as a marker for IPMN progression and identified several DLGAP5-dependent secreted proteins that may serve as surrogate biomarkers for stratification of the patients with IPMN. Prospective validation in cystic fluid samples is ongoing.

DISKS LARGE-ASSOCIATED PROTEIN 5 (DLGAP5) IS A PUTATIVE PROGNOSTIC BIOMARKER FOR INTRADUCTAL PAPILLARY MUCINOUS NEOPLASM (IPMN) OF PANCREAS

Peter Macinga 1, Lucia Csergeova 2, Ondrej Fabian 3, Eva Sticova 4, Jiri Zahradnik 2, Martin Kveton 4, Tomas Hucl 5, Radoslav Janostiak 2

1 Institute For Clinical And Experimental Medicine, Prague, Czechia

2 BIOCEV, First Faculty of Medicine, Charles University, Prague, Czechia

3 Third Faculty of Medicine, Charles University and Thomayer University Hospital, Prague, Czechia|||Institute for Clinical and Experimental Medicine, Prague, Czechia

4 Institute for Clinical and Experimental Medicine, Prague, Czechia

5 Institute for Clinical and Experimental Medicine (IKEM), Prague, Czechia

Conference

UEG Week Berlin 2025

Topics

Pancreas

Submission format

Abstract

Session

Risk factors for pancreatic cancer IPMN: Diabetes and genetics (Posters)

Citation

United European Gastroenterology Journal 2025; 13 (Supplement 8)

Published

2025

UEG Presentation

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AN IMMUNE RESPONSIVE TUMOR MICROENVIRONMENT IMPRINTS INTO PBMCS AND ALLOWS TO PREDICT PATIENT OUTCOME USING MACHINE LEARNING IN ADVANCED PANCREATIC CANCER: LESSONS FROM THE PREDICT TRIAL

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Introduction

Prognosis in advanced pancreatic ductal adenocarcinoma (aPDAC) is particularly poor. Few patients benefit from second-line chemotherapy, while most experience side effects, highlighting the need for biomarkers. The PREDICT trial examined in aPDAC patients whether first-line time-to-treatment failure (TTF1) predicts second-line treatment failure (TTF2) but found no association. In our translational study we hypothesized that the responsiveness of the tumor immune microenvironment (TiME) might determine second-line therapy outcome and examined whether treatment-naive tissue immune features are also reflected in the peripheral blood after one line of chemotherapy.

Aims & Methods

In the PREDICT trial, patients received 5-FU/LV plus nanoliposomal irinotecan second-line after gemcitabine/nab-paclitaxel (Gem-nabPac) first-line therapy. We stratified PREDICT patients by shortest vs. longest TTF2. Treatment-naive primary tumor tissues from 20 patients were analyzed by NanoString transcriptomic profiling and multiplexed immunohistochemistry to characterize differences in the TiME. Peripheral blood mononuclear cells (PBMCs) were obtained immediately before second-line therapy from 82 patients and analyzed using multiparameter flow cytometry with an automated gating procedure and gene expression profiling via RT-qPCR. A feature selection and machine learning pipeline integrated PBMC and clinical data to derive a minimal predictor for TTF2 outcome and prognosis that was validated across different treatment regimens on an independent external cohort of 30 patients with aPDAC.

Results

Treatment-naive tumors of patients in the long-TTF2 group exhibited an immune-active (“hot”) TiME with CXCR3⁺CD8⁺ cytotoxic T cell infiltration into the cancer cell compartment via CXCR3-dependant inflammatory chemokine signaling. Analysis of PBMCs obtained directly before second-line therapy showed that these immune features were sustained over one line of treatment and reflected in PBMCs. A PBMC-based machine learning model (“TTF2Pred”) identified a 7-feature immune signature that accurately distinguished long vs. short TTF2 patient groups outperforming multivariate clinical data or CA19-9 benchmark models. TTF2Pred robustness was confirmed by external validation and showed the best overall performance post-Gem-nabPac first-line treatment. Among the seven TTF2Pred features, long-TTF2 patients exhibited more circulating CXCR3⁺ activated T cells and plasmacytoid dendritic cells, while short-TTF2 patients had more circulating platelet–leukocyte (platelet-monocyte, platelet-neutrophil) aggregates and exhausted T cells.

Conclusion

PDAC patients with an immune-active, treatment-naïve TiME have a better outcome on second-line therapy, and these tissue characteristics are reflected in circulating PBMCs after one line of chemotherapy. A parsimonious circulating PBMC-based signature offers a minimally invasive predictor of second-line outcome that could inform personalized therapy.

AN IMMUNE RESPONSIVE TUMOR MICROENVIRONMENT IMPRINTS INTO PBMCS AND ALLOWS TO PREDICT PATIENT OUTCOME USING MACHINE LEARNING IN ADVANCED PANCREATIC CANCER: LESSONS FROM THE PREDICT TRIAL

Anton Lahusen 1, Manfred P. Lutz 2, Rui Fang 3, Martina Kirchner 4, Sarah Wisser 5, Klaus Kluck 4, Meinolf Karthaus 6, Andreas Schwarzer 7, Gabriele Siegler 8, Thomas J. Ettrich 1, Alexander Becher 1, Sabine Hoefling 9, Jens T. Siveke 3, Jan Budczies 4, Andrea Tannapfel 5, Albrecht Stenzinger 4, Phyllis Fung-Yi Cheung 3, Tim Eiseler 1, Thomas Seufferlein 1

1 University Hospital Ulm, Ulm, Germany

2 Caritas Klinikum Saarbrücken St. Theresia, Saarbrücken, Germany

3 University Hospital Essen, Essen, Germany

4 Heidelberg University Hospital, Heidelberg, Germany

5 Ruhr University, Bochum, Germany

6 München Klinik Harlaching and Neuperlach, München, Germany

7 Onkopraxis Probstheida, Leipzig, Germany

8 Klinikum Nürnberg Nord/Paracelsus Medizinische Privatuniversität, Nürnberg, Germany

9 CROLLL GmbH, Nürnberg, Germany

Event

UEG Week Berlin 2025

Topics

Digestive Oncology Immunology Pancreas Surgery

Submission format

Abstract

Session

PDAC surveillance and treatment: The how's and why's

Citation

United European Gastroenterology Journal 2025; 13 (Supplement 8)

Published

2025

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