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

Area of Science:

• Oncology
• Genomics
• Immunology

Background:

• Patient-derived xenograft (PDX) models lack patient-specific mutations and human leukocyte antigen (HLA) diversity, failing to replicate the tumor microenvironment (TME).
• These limitations lead to over 80% immunotherapy prediction failure in clinical translation.

Purpose of the Study:

• To develop an advanced preclinical model framework, "Tumor Model 2.0", addressing the deficiencies of conventional PDX models.
• To create high-fidelity, patient-specific cancer models for improved immunotherapy prediction and personalized medicine.

Main Methods:

• Integration of multi-omics data (whole-genome, transcriptomic, proteomic) with precision genome editing (CRISPR-Cas9, Prime Editing).
• Development of an organoid-animal coupling platform with stepwise immune system construction and microenvironment remodeling.
• Validation in large animal models to create programmable, patient-specific digital twins.

Main Results:

• Reconstruction of patient-specific mutations across multiple biological layers in novel preclinical models.
• Successful creation of programmable, patient-specific digital twins with enhanced TME and HLA diversity.
• Demonstration of a viable framework for N-of-1 clinical trials.

Conclusions:

• Tumor Model 2.0 offers a significant advancement over traditional PDX models for cancer research.
• This framework bridges the gap between preclinical studies and clinical precision oncology, enhancing immunotherapy development.
• The programmable, patient-specific digital twins facilitate personalized N-of-1 clinical trials.