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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.