Page 116 - OR-1-2

tumor evolution, screening anti-cancer drugs and cancer Availability of data
therapies, and achieving precision medicine.
Not applicable.
5. Conclusion
References
Despite many limitations, the development of organoid 1. Bray F, Laversanne M, Sung H, et al. Global cancer statistics
models is of great significance in the field of cancer 2022: GLOBOCAN estimates of incidence and mortality
research. Variations in patients’ drug sensitivity have worldwide for 36 cancers in 185 countries. CA Cancer J Clin.
an impact on the clinical transformation after drug 2024;74:229-263.
screening with organoids. Therefore, there is an urgent
need to develop new technologies and platforms based doi: 10.3322/caac.21834
on patient-derived samples in the future to overcome 2. Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024.
the limitations of organoid culture while improving the CA Cancer J Clin. 2024;74:2-49.
accuracy of drug screening. In addition to precision doi: 10.3322/caac.21820
medicine and personalized treatment, the high similarity
between organoids and primitive tumors can also 3. Miserocchi G, Mercatali L, Liverani C, et al. Management
and potentialities of primary cancer cultures in preclinical
provide great help for basic cancer research. In the and translational studies. J Transl Med. 2017;15:229.
future, organoid models can also be applied to research
in the fields of infectious diseases, autoimmune diseases, doi: 10.1186/s12967-017-1328-z
proteomics analysis, and immunotherapy. Organoid 4. Hait WN. Anticancer drug development: The grand
technology can significantly promote the development challenges. Nat Rev Drug Discov. 2010;9:253-254.
of new drugs, thereby improving patient survival and doi: 10.1038/nrd3144
prognosis.
5. MKapałczyńska M, Kolenda T, Przybyła W, et al. 2D and 3D
Acknowledgments cell cultures - a comparison of different types of cancer cell
cultures. Arch Med Sci. 2016;14:910.
The authors L.Q.C. and Y.H.X. express thanks for the
teaching assistantship and fellowship, respectively, from doi: 10.5114/aoms.2016.63743
the Department of Pharmaceutical Sciences, St. John’s 6. Cheon DJ, Orsulic S. Mouse models of cancer. Annu Rev
University. Pathol. 2011;6:95-119.
Funding doi: 10.1146/annurev.pathol.3.121806.154244
7. Liu Y, Wu W, Cai C, Zhang H, Shen H, Han Y. Patient-derived
None. xenograft models in cancer therapy: Technologies and
applications. Signal Transduct Target Ther. 2023;8(1):160.
Conflict of interest
doi: 10.1038/s41392-023-01419-2
Zhe-Sheng Chen is an Associate Editor of this journal but
was not in any way involved in the editorial and peer-review 8. Kamb A. What’s wrong with our cancer models? Nat Rev
Drug Discov. 2005;4:161-165.
process conducted for this paper, directly or indirectly.
Separately, other authors declared that they have no known doi: 10.1038/nrd1635
competing financial interests or personal relationships that 9. Caponigro G, Sellers WR. Advances in the preclinical testing
could have influenced the work reported in this paper. of cancer therapeutic hypotheses. Nat Rev Drug Discov.
2011;10:179-187.
Author contributions
doi: 10.1038/nrd3385
Conceptualization: Lu-Qi Cao, Yuhao Xie, Zhe-Sheng Chen 10. Drost J, Clevers H. Organoids in cancer research. Nat Rev
Visualization: Lu-Qi Cao, Yuhao Xie Cancer. 2018;18(7):407-418.
Writing – original draft: Lu-Qi Cao, Yuhao Xie
Writing – review & editing: Yuhong Liu, John Wurpel, Leli doi: 10.1038/s41568-018-0007-6
Zeng, Zhe-Sheng Chen 11. Ben-David U, Siranosian G, Ha H, et al. Genetic and
transcriptional evolution alters cancer cell line drug response.
Ethics approval and consent to participate Nature. 2018;560:325-330.
Not applicable. doi: 10.1038/s41586-018-0409-3
Consent for publication 12. Jin J, Yoshimura K, Sewastjanow-Silva M, Song S, Ajani JA.
Challenges and prospects of patient-derived xenografts for
Not applicable. cancer research. Cancers (Basel). 2023;15:4352.

Volume 1 Issue 2 (2025) 10 doi: 10.36922/OR025050008