Grasping these fundamental characteristics and recapitulating such evolutionary characteristics of human cancer is the critical prerequisite for cancer models to accurately reflect the response cancer and tackle the cancer treatment challenges.Īccordingly, animal models such as genome-edited mouse models, patient-derived organoids, and patient-derived xenografts (PDX) have been used to study cancer biology and capture the cancer landscape. 6 Besides, cancer spatial architecture is shaped by the interaction among different cell components and tissue structures, such as vascular distribution. 5 Moreover, intratumor spatial and temporal heterogeneity added complexity to cancer, which refers to the diverse cancer cell phenotypes and different states of cancer cells within a single patient caused by cancer evolution and anti-cancer treatment selection. During cancer progression, genetic and epigenetic aberrations lead to distinct genomic landscapes among patients, and different treatments further fuel genomic evolution and remodeling. Oncogenesis is a dynamic process that results from many intertwined factors. 4 These problems are largely due to the lack of research tools to reveal the genuine status of cancer in real-world patients. Although numerous prospective drugs against cancer have been developed and shown promising therapeutic efficacy in vitro, only a few of them have been proven safe and effective in the context of complex in vivo experiments. 3 Moreover, the remaining cancer patients are in urgent need of new pharmaceuticals. 2 They need guidance regarding the choice of next-line therapies, which relies on the dynamic detection of the cancer status. 1 Besides, the patients who respond to these therapies must deal with resistance and cancer recurrence. However, current biomarkers and signatures are insufficient to reflect the genuine cancer status, let alone classify the patients accurately. Therefore, robust biomarkers are necessary to accurately select the patients who will respond to or resist these therapies. First, only a small proportion of cancer patients benefit from the drugs. Nonetheless, many problems have restrained improvement in the prognosis of cancer patients. In the realm of cancer treatment, the advent of targeted therapies and immunotherapies has greatly enriched the arsenal against cancer and provided patients with better therapeutic outcomes and milder side effects. Finally, we delineated the broad application of PDX models in chemotherapy, targeted therapy, immunotherapy, and other novel therapies. Subsequently, the review presents the strengths and weaknesses of PDX models and highlights the integration of novel technologies in PDX model research. In this review, we gave an overview of the history of PDX models and the process of PDX model establishment. These irreplaceable advantages make PDX models an ideal choice in cancer treatment studies, such as preclinical trials of novel drugs, validating novel drug combinations, screening drug-sensitive patients, and exploring drug resistance mechanisms. Optimized PDX engraftment procedures and modern technologies such as multi-omics and deep learning have enabled a more comprehensive depiction of the PDX molecular landscape and boosted the utilization of PDX models. Moreover, PDX models retain the genomic features of patients across different stages, subtypes, and diversified treatment backgrounds. Patient-derived xenograft (PDX) models, in which tumor tissues from patients are implanted into immunocompromised or humanized mice, have shown superiority in recapitulating the characteristics of cancer, such as the spatial structure of cancer and the intratumor heterogeneity of cancer.
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