Lung Cancer Translational Models: Bridging Preclinical Science and Human Disease​

Lung cancer remains the leading cause of cancer-related mortality worldwide, underscoring the urgent need for improved therapeutic strategies grounded in robust biological understanding. Translational models; including cell lines, organoids, patient-derived xenografts, and genetically engineered animal models, are central to bridging basic science discoveries and clinical application. However, no single preclinical system fully captures the genetic, epigenetic, and microenvironmental complexity of human lung cancer, contributing to frequent translational failure. Advances in model development, integration of human-relevant systems, and cross-validation with clinical data offer new opportunities to enhance predictive power and therapeutic relevance. This Research Topic focuses on innovative lung cancer translational models, their strengths and limitations, and how they can more effectively inform human disease biology, treatment response, and resistance mechanisms, ultimately accelerating progress from bench to bedside.Despite significant advances in lung cancer genomics and targeted therapies, the translation of preclinical findings into durable clinical benefit remains inconsistent. A central challenge lies in the limited ability of existing models to faithfully recapitulate tumor heterogeneity, evolutionary dynamics, immune interactions, and treatment response observed in patients. This Research Topic aims to address this gap by critically evaluating current lung cancer translational models and highlighting strategies to improve their relevance and predictive value. We seek contributions that integrate innovative model systems, leverage patient-derived data, and apply rigorous cross-model and clinical validation. By fostering dialogue between basic, translational, and clinical researchers, this collection aims to define best practices and identify emerging approaches that enhance the alignment between preclinical research and human lung cancer, ultimately improving therapeutic development and patient outcomes.The scope of this Research Topic encompasses the development, application, and evaluation of translational models in lung cancer research. We welcome contributions addressing in vitro, ex vivo, and in vivo systems, including 2D and 3D culture models, organoids, co-culture systems, patient-derived xenografts, genetically engineered and humanized animal models, and computational or integrative modeling approaches. Topics of interest include model fidelity to human disease, representation of tumor heterogeneity and microenvironmental interactions, modeling of immune responses, mechanisms of therapy resistance, and strategies for model validation using clinical data. We invite Original Research, Reviews, mini-Reviews, and methods articles that provide conceptual, technical, or translational insights aimed at strengthening.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 journal.Dr. Mahdi declares to have received research funding (to Institution, University of Limerick) from Stryker Corporation.Dr. Malacrida declares to have received funding from AstraZeneca. Topic editors: Catriona Dowling, Beatrice Malacrida Source: https://www.frontiersin.org/research-topics/80148/lung-cancer-translational-models-bridging-preclinical-science-and-human-disease