Harnessing PDX and PDX 2.0: the next-generation paradigm for precision oncology and translational breakthroughs

Cancer research has achieved remarkable breakthroughs over the past decades with the aid of patient-derived xenograft (PDX) models. However, the limitations of conventional PDX models in hindering clinical translation have become increasingly apparent. In 2025, the National Institutes of Health (NIH) announced a funding shift away from exclusive reliance on animal models without justified integration of novel alternative methods (NAMs), human-relevant modeling approaches. Nevertheless, PDX models cannot be fully replaced currently due to the lingering immaturity and uncertainties of NAMs technologies, indicating that a complete non-animal research paradigm will require sustained methodological development. Therefore, developing an innovative, optimized PDX model to navigate this transitional phase remains the holy grail of preclinical cancer research. Herein, we propose PDX 2.0, a novel conceptual framework that advances conventional PDX models via the systematic integration of NAMs and complementary technologies, thereby enabling more efficient and precise cancer research. This review first delineates the core determinants, major applications, and critical limitations of traditional PDX models, then defines the conceptual architecture and distinctive characteristics of PDX 2.0. We further highlight emerging applications of this framework in high-throughput drug screening, biomarker discovery, and adaptive therapeutic evaluation, positioning PDX 2.0 as a critical evolution of PDX-based research to better support clinically actionable precision oncology.

Authors

Institutions

Publication Details

Journal
Molecular Cancer
Published
2026-02-17
DOI
https://doi.org/10.1186/s12943-026-02594-w
Primary Topic
Cancer Cells and Metastasis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Harnessing PDX and PDX 2.0: the next-generation paradigm for precision oncology and translational breakthroughs

Chengli Jian, Wantao Wu, Hao Zhang, Nan Zhang et al.
Molecular Cancer
Cancer Cells and Metastasis
article

Harnessing PDX and PDX 2.0: the next-generation paradigm for precision oncology and translational breakthroughs

Chengli Jian, Wantao Wu, Hao Zhang, Nan Zhang, Hao Fu, Zhiwei Xia, Quan Cheng, Peng Luo, Zaoqu Liu
article en

Abstract

Cancer research has achieved remarkable breakthroughs over the past decades with the aid of patient-derived xenograft (PDX) models. However, the limitations of conventional PDX models in hindering clinical translation have become increasingly apparent. In 2025, the National Institutes of Health (NIH) announced a funding shift away from exclusive reliance on animal models without justified integration of novel alternative methods (NAMs), human-relevant modeling approaches. Nevertheless, PDX models cannot be fully replaced currently due to the lingering immaturity and uncertainties of NAMs technologies, indicating that a complete non-animal research paradigm will require sustained methodological development. Therefore, developing an innovative, optimized PDX model to navigate this transitional phase remains the holy grail of preclinical cancer research. Herein, we propose PDX 2.0, a novel conceptual framework that advances conventional PDX models via the systematic integration of NAMs and complementary technologies, thereby enabling more efficient and precise cancer research. This review first delineates the core determinants, major applications, and critical limitations of traditional PDX models, then defines the conceptual architecture and distinctive characteristics of PDX 2.0. We further highlight emerging applications of this framework in high-throughput drug screening, biomarker discovery, and adaptive therapeutic evaluation, positioning PDX 2.0 as a critical evolution of PDX-based research to better support clinically actionable precision oncology.

Molecular CancerVol. 25(1)
Central South University (CN), Changsha Medical University (CN), Hunan Normal University (CN), Dalian Medical University (CN), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Second Affiliated Hospital of Chongqing Medical University (CN), Zhujiang Hospital (CN), Chongqing Institute of Green and Intelligent Technology (CN), National Clinical Research (US), Xiangya Hospital Central South University (CN), First People's Hospital of Chongqing (CN), Huazhong University of Science and Technology (CN), Chongqing Medical University (CN)
Partnerships for the goals
Openalex Percentile: Top 54%
Cancer Cells and Metastasis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.