Neufeld Lena, Yeini Eilam, Pozzi Sabina, Satchi-Fainaro Ronit
Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
Sagol School of Neurosciences, Tel Aviv University, Tel Aviv, Israel.
Nat Rev Cancer. 2022 Dec;22(12):679-692. doi: 10.1038/s41568-022-00514-w. Epub 2022 Oct 24.
Effort invested in the development of new drugs often fails to be translated into meaningful clinical benefits for patients with cancer. The development of more effective anticancer therapeutics and accurate prediction of their clinical merit remain urgent unmet medical needs. As solid cancers have complex and heterogeneous structures composed of different cell types and extracellular matrices, three-dimensional (3D) cancer models hold great potential for advancing our understanding of cancer biology, which has been historically investigated in tumour cell cultures on rigid plastic plates. Advanced 3D bioprinted cancer models have the potential to revolutionize the way we discover therapeutic targets, develop new drugs and personalize anticancer therapies in an accurate, reproducible, clinically translatable and robust manner. These ex vivo cancer models are already replacing existing in vitro systems and could, in the future, diminish or even replace the use of animal models. Therefore, profound understanding of the differences in tumorigenesis between 2D, 3D and animal models of cancer is essential. This Review presents the state of the art of 3D bioprinted cancer modelling, focusing on the biological processes that underlie the molecular mechanisms involved in cancer progression and treatment response as well as on proteomic and genomic signatures.
投入新药研发的努力往往未能转化为癌症患者切实的临床益处。开发更有效的抗癌疗法并准确预测其临床价值仍是亟待满足的医疗需求。由于实体癌具有由不同细胞类型和细胞外基质组成的复杂异质结构,三维(3D)癌症模型在增进我们对癌症生物学的理解方面具有巨大潜力,而癌症生物学一直以来都是在刚性塑料板上的肿瘤细胞培养中进行研究的。先进的3D生物打印癌症模型有可能彻底改变我们发现治疗靶点、开发新药以及以准确、可重复、临床可转化且稳健的方式实现抗癌疗法个性化的方式。这些体外癌症模型已经在取代现有的体外系统,并且在未来可能会减少甚至取代动物模型的使用。因此,深入了解二维、三维和癌症动物模型在肿瘤发生方面的差异至关重要。本综述介绍了3D生物打印癌症建模的最新进展,重点关注癌症进展和治疗反应所涉及分子机制背后的生物学过程以及蛋白质组学和基因组特征。