表型可塑性最近被纳入癌症的标志。这种可塑性可以沿着许多相互关联的轴表现出来，例如干性和分化、药物敏感和耐药状态以及上皮细胞和间充质细胞状态之间。尽管人们越来越多地接受表型可塑性作为癌症的标志，但这一过程的动力学仍然知之甚少。特别是，预测性理解单个癌细胞和细胞群如何响应当前和过去环境刺激的强度和/或持续时间动态转换其表型所需的知识仍然远未完成。在这里，我们使用两个例子从系统水平的角度介绍了表型可塑性的最新研究：癌症中的上皮间质可塑性和黑色素瘤中的表型转换。我们强调综合计算实验方法如何帮助揭示不同癌症表型可塑性的特定动态标志，以解决以下问题：a）存在多少种不同的细胞状态或表型？ b）这些细胞状态之间的转变的可逆性如何，以及哪些因素控制可逆性的程度？ c) 细胞间通讯如何能够改变细胞状态转换的速率并实现不同的表型异质性模式？了解表型可塑性的这些动态特征可能是将癌症治疗范式从反应性转变为更具预测性、主动性的方法的关键组成部分。版权所有 © 2023 Elsevier Ltd. 保留所有权利。

Phenotypic plasticity was recently incorporated as a hallmark of cancer. This plasticity can manifest along many interconnected axes, such as stemness and differentiation, drug-sensitive and drug-resistant states, and between epithelial and mesenchymal cell-states. Despite growing acceptance for phenotypic plasticity as a hallmark of cancer, the dynamics of this process remains poorly understood. In particular, the knowledge necessary for a predictive understanding of how individual cancer cells and populations of cells dynamically switch their phenotypes in response to the intensity and/or duration of their current and past environmental stimuli remains far from complete. Here, we present recent investigations of phenotypic plasticity from a systems-level perspective using two exemplars: epithelial-mesenchymal plasticity in carcinomas and phenotypic switching in melanoma. We highlight how an integrated computational-experimental approach has helped unravel insights into specific dynamical hallmarks of phenotypic plasticity in different cancers to address the following questions: a) how many distinct cell-states or phenotypes exist?; b) how reversible are transitions among these cell-states, and what factors control the extent of reversibility?; and c) how might cell-cell communication be able to alter rates of cell-state switching and enable diverse patterns of phenotypic heterogeneity? Understanding these dynamic features of phenotypic plasticity may be a key component in shifting the paradigm of cancer treatment from reactionary to a more predictive, proactive approach.Copyright © 2023 Elsevier Ltd. All rights reserved.