Leong Tze Ker Matthew, Lo Wen Shern, Lee Wei En Zen, Tan Benedict, Lee Xing Zhao, Lee Li Wen Justina Nadia, Lee Jia-Ying Joey, Suresh Nivedita, Loo Lit-Hsin, Szu Evan, Yeong Joe
Lee Kong Chian School of Medicine, Nanyang Technological University, Headquarters & Clinical Sciences Building, 11 Mandalay Road, Singapore 308232, Singapore.
Institute of Molecular and Cell Biology (IMCB), Agency of Science, Technology and Research (A*STAR), 61 Biopolis Drive, Proteos, Singapore 138673, Singapore.
Adv Drug Deliv Rev. 2021 Oct;177:113959. doi: 10.1016/j.addr.2021.113959. Epub 2021 Sep 1.
Cancer is the leading cause of death worldwide. Unfortunately, efforts to understand this disease are confounded by the complex, heterogenous tumor microenvironment (TME). Better understanding of the TME could lead to novel diagnostic, prognostic, and therapeutic discoveries. One way to achieve this involves in vitro tumor models that recapitulate the in vivo TME composition and spatial arrangement. Here, we review the potential of harnessing in vitro tumor models and artificial intelligence to delineate the TME. This includes (i) identification of novel features, (ii) investigation of higher-order relationships, and (iii) analysis and interpretation of multiomics data in a (iv) holistic, objective, reproducible, and efficient manner, which surpasses previous methods of TME analysis. We also discuss limitations of this approach, namely inadequate datasets, indeterminate biological correlations, ethical concerns, and logistical constraints; finally, we speculate on future avenues of research that could overcome these limitations, ultimately translating to improved clinical outcomes.
癌症是全球首要死因。不幸的是，由于肿瘤微环境（TME）复杂且异质性强，了解这种疾病的努力受到了困扰。更好地理解TME可能会带来新的诊断、预后和治疗方法。实现这一目标的一种方法是采用能够重现体内TME组成和空间排列的体外肿瘤模型。在此，我们综述利用体外肿瘤模型和人工智能来描绘TME的潜力。这包括以一种超越以往TME分析方法的（iv）全面、客观、可重复且高效的方式（i）识别新特征，（ii）研究高阶关系，以及（iii）分析和解释多组学数据。我们还讨论了这种方法的局限性，即数据集不足、生物学相关性不确定、伦理问题和后勤限制；最后，我们推测了未来可能克服这些局限性的研究途径，最终转化为改善临床结果。
