Prediction of metastatic potential of heterogeneous pancreatic ductal adenocarcinoma through gradient-based algorithms

Pancreatic ductal adenocarcinoma carries a dismal prognosis, with five-year survival below 10 % due to late presentation, aggressive nature, and profound intratumor heterogeneity. Existing prognostic models fail to account for the dynamic interplay between subclonal evolution and the tumor microenvi...

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Published in:Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.] Vol. 25; no. 5; pp. 694 - 708
Main Authors: Rema, Parvathy, Ramesh, Aravind, Appukuttan, Murali, Manju, B.R
Format: Journal Article
Language:English
Published: Switzerland Elsevier B.V 01.08.2025
Subjects:
Agent-based models
Algorithms
Artificial intelligence
Carcinogenesis
Carcinoma, Pancreatic Ductal - pathology
Computer Simulation
Continuum-based models
Humans
Intratumor heterogeneity
Mathematical modeling
Neoplasm Metastasis
Pancreatic ductal adenocarcinoma
Pancreatic Neoplasms - pathology
Prognosis
Tumor Microenvironment
Agent-based models
Intratumor heterogeneity
Continuum-based models
Pancreatic ductal adenocarcinoma
Mathematical modeling
Artificial intelligence
Carcinogenesis
ISSN:1424-3903, 1424-3911, 1424-3911
Online Access:Get full text
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Summary:Pancreatic ductal adenocarcinoma carries a dismal prognosis, with five-year survival below 10 % due to late presentation, aggressive nature, and profound intratumor heterogeneity. Existing prognostic models fail to account for the dynamic interplay between subclonal evolution and the tumor microenvironment, limiting their clinical utility for risk stratification and therapy guidance. An in silico, multiscale framework was developed, combining an agent-based model of subclone proliferation, migration, quiescence, and apoptosis with continuum reaction–diffusion equations for oxygen, nutrients, extracellular matrix, and chemoattractants. Four clinically relevant microenvironmental scenarios were simulated to generate spatiogenetic signatures of subclonal adaptation. These signatures were then sequentially input into three gradient-boosting classifiers—CatBoost, LightGBM, and XGBoost—to predict each subclone's metastatic potential. All hybrid pipelines demonstrated robust discrimination of high-risk subclones, with XGBoost achieving the highest sensitivity (92 %) and specificity (89 %) in cross-validation. The model accurately recapitulated PDAC's known clinical features, such as hypoxia-driven invasive fronts and desmoplastic stroma–associated resistance. Importantly, it uncovered novel biomarker signatures—combinations of genetic mutations and microenvironmental factors—that correlated with early metastatic seeding in simulated cohorts. This work introduces a novel multiscale hybrid framework that is original both in its formulation integrating agent-based, continuum, and immune dynamics and in its application, where simulated biological signatures are used to train gradient boosting models for accurate prediction of metastatic potential.
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ISSN:1424-3903
1424-3911
1424-3911
DOI:10.1016/j.pan.2025.06.017