The DOI matches its Crossref record; 288 of the 288 references it cites were found in a registry or an index.
Patient-derived xenograft models in cancer therapy: technologies and applications
Open accessAbstract
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. Moreover, PDX models retain the genomic features of patients across different stages, subtypes, and diversified treatment backgrounds. 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. 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. In this review, we gave an overview of the history of PDX models and the process of PDX model establishment. Subsequently, the review presents the strengths and weaknesses of PDX models and highlights the integration of novel technologies in PDX model research. Finally, we delineated the broad application of PDX models in chemotherapy, targeted therapy, immunotherapy, and other novel therapies.
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Cited by
Allgayer, H., Mahapatra, S., Mishra, B., Swain, B., Saha, S., Khanra, S., Kumari, K., Panda, V. K., Malhotra, D., Patil, N. S., et al. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02338-2 · Cited by 127
Metadata
- Authors
- Liu, Y., Wu, W., Cai, C., Zhang, H., Shen, H., & Han, Y.
- Venue
- Signal Transduction and Targeted Therapy
- Published
- 2023-04-12
- Volume
- 8
- Issue
- 1
- Pages
- 160-160
- ISSN
- 2059-3635 (ISSN-L)
- OpenAlex
- W4365148379
- Cited by
- 494
- References
- 288
- FWCI
- 51.04
- Type
- journal article
- Language
- en
- Open access
- gold
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- Liu, Y., Wu, W., Cai, C., Zhang, H., Shen, H., & Han, Y. (2023). Patient-derived xenograft models in cancer therapy: technologies and applications. Signal Transduction and Targeted Therapy. https://doi.org/10.1038/s41392-023-01419-2
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
