Abstract
Three-dimensional (3D) tumor models prepared from patient-derived cells have been reported to imitate some of the biological development processes of in situ tumors in vitro. These 3D tumor models share several important characteristics with their in vivo tumor counterparts. Accordingly, their applications in tumor modeling, drug screening, and precision-targeted treatment are promising. However, the establishment of tumor models is subject to several challenges, including advancements in scale size, repeatability, structural precision in time and space, vascularization, and the tumor microenvironment. Recently, bioprinting technologies enabling the editorial arrangement of cells, factors, and materials have improved the simulation of tumor models in vitro. Among the 3D bioprinted tumor models, the organoid model has been widely appreciated for its advantages of maintaining high heterogeneity and capacity for simulating the developmental process of tumor tissues. In this review, we outline approaches and potential prospects for tumor model bioprinting and discuss the existing bioprinting technologies and bioinks in tumor model construction. The multidisciplinary combination of tumor pathology, molecular biology, material science, and additive manufacturing will help overcome the barriers to tumor model construction by allowing consideration of the structural and functional characteristics of in vitro models and promoting the development of heterogeneous tumor precision therapies.
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The authors appreciated the financial support from the National Key R&D Program of China (No. 2018YFA0703000), the National Natural Science Foundation of China (No. 82072412), the Translation Medicine National Key Science and Technology Infrastructure (Shanghai) Open Project (No. TMSK-2020-118), the Lingang Laboratory “Seeking Outstanding Youth Program” Open Project (No. LG-QS-202206-04), and the Shanghai Municipal Natural Science Foundation (No. 19ZR1429100).
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Zhang, C., Qiu, X., Dai, Y. et al. The prospects for bioprinting tumor models: recent advances in their applications. Bio-des. Manuf. 6, 661–675 (2023). https://doi.org/10.1007/s42242-023-00247-1
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DOI: https://doi.org/10.1007/s42242-023-00247-1