From "Simulation" to "Mirror": Gene editing and humanization redefines the next-generation precision oncology animal model.

Abstract

Patient-derived xenograft (PDX) models, although conventionally used in oncology, exhibit critical limitations: they frequently lose patient-specific genetic mutations and lack the human leukocyte antigen (HLA) diversity essential for immune recognition, and fail to recapitulate the human tumor microenvironment (TME). These deficiencies contribute to immunotherapy prediction failure rates exceeding 80% in clinical translation. To address these gaps, we propose a Tumor Model 2.0 framework. This framework integrates multi-omics data (whole-genome, transcriptomic, and proteomic) with precision genome editing technologies (CRISPR-Cas9 and Prime Editing) to reconstruct patient-specific mutations across multiple biological layers. Employing an organoid-animal coupling platform with stepwise immune system construction and microenvironment remodeling-subsequently validated in large animals-the framework enables the creation of programmable, patient-specific digital twins. These high-fidelity models support personalized N-of-1 clinical trials, bridging the gap between preclinical research and clinical precision oncology.