Unraveling Pharmacological Resistance in Gastrointestinal and Pancreatic Malignancies: From Next-Generation Models to Dynamic Biomarkers

The clinical advent of mutant-specific KRAS and next-generation pan-RAS inhibitors has fundamentally disrupted the therapeutic paradigm for aggressive gastrointestinal malignancies, led by pancreatic ductal adenocarcinoma (PDAC). However, whether deploying these novel targeted agents or standard multi-agent chemotherapies, clinical durability is inevitably limited by therapeutic resistance. Rather than a static phenomenon, this treatment failure is governed by a rapid evolutionary race: tumors survive through a coordinated combination of cell-intrinsic signaling rewiring and non-autonomous protection mediated by the microenvironmental secretome. Capturing this real-time adaptation requires moving beyond static tissue snapshots into integrated, dynamic diagnostic and preclinical platforms. The primary objective of this Research Topic is to assemble a high-impact collection of interdisciplinary research dedicated to refining clinical outcomes in aggressive gastrointestinal and pancreatic malignancies. This collection seeks to bridge surgical oncology, translational pharmacology, and data science by investigating both the cell-intrinsic molecular rewiring and the extrinsic, secretome-mediated protection driving resistance to conventional chemotherapies and novel targeted agents, such as KRAS and pan-RAS inhibitors. Specifically, we aim to highlight how liquid biopsy modalities can be leveraged for early detection, the evaluation of surgical resectability, and the real-time tracking of pharmacological evasion. We highly encourage studies evaluating novel therapeutic strategies and resistance pathways using state-of-the-art ex-vivo and in-vitro platforms, such as patient-derived organoids and 3D organotypic cultures. Crucially, to capture the full complexity of tumor heterogeneity, this collection welcomes innovative in silico workflows that utilize large patient cohorts and multi-omics datasets, analyzed using advanced bioinformatics and artificial intelligence (AI) approaches. Ultimately, this topic aims to identify complementary, data-driven diagnostic and therapeutic pathways that translate directly into enhanced patient stratification. This collection focuses on uncovering the biological, mechanical, and computational dimensions of therapeutic resistance across gastrointestinal and pancreatic cancers. We warmly welcome Original Research, Review, Mini Review, and Perspective articles on themes including, but not limited to: - Molecular, cellular, and genetic mechanisms of resistance to conventional chemotherapies and novel allele-specific KRAS (e.g., G12D, G12C) or pan-RAS inhibitors.- Utilization of patient-derived organoids, 3D spheroids, and ex-vivo organotypic tissue slices to model drug pharmacodynamics and screen for synergy. - Liquid biopsy applications—specifically extracellular vesicles (EVs), tumor-educated platelets (TEPs), and circulating nucleic acids—to monitor real-time resistance kinetics. - Innovative bioinformatic pipelines and AI-driven workflows utilizing large-scale clinical or omics datasets to map chemoresistance patterns. - Extrinsic microenvironmental and host-mediated mechanisms that shield gastrointestinal malignancies from systemic therapies.Please note: manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this Research Topic. Topic editors: Elisa Giovannetti, Annalisa Comandatore Source: https://www.frontiersin.org/research-topics/83345/unraveling-pharmacological-resistance-in-gastrointestinal-and-pancreatic-malignancies-from-next-generation-models-to-dynamic-biomarkers