Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, India.
Essays Biochem. 2022 Sep 16;66(4):387-398. doi: 10.1042/EBC20220038.
Intratumoral heterogeneity can exist along multiple axes: Cancer stem cells (CSCs)/non-CSCs, drug-sensitive/drug-tolerant states, and a spectrum of epithelial-hybrid-mesenchymal phenotypes. Further, these diverse cell-states can switch reversibly among one another, thereby posing a major challenge to therapeutic efficacy. Therefore, understanding the origins of phenotypic plasticity and heterogeneity remains an active area of investigation. While genomic components (mutations, chromosomal instability) driving heterogeneity have been well-studied, recent reports highlight the role of non-genetic mechanisms in enabling both phenotypic plasticity and heterogeneity. Here, we discuss various processes underlying phenotypic plasticity such as stochastic gene expression, chromatin reprogramming, asymmetric cell division and the presence of multiple stable gene expression patterns ('attractors'). These processes can facilitate a dynamically evolving cell population such that a subpopulation of (drug-tolerant) cells can survive lethal drug exposure and recapitulate population heterogeneity on drug withdrawal, leading to relapse. These drug-tolerant cells can be both pre-existing and also induced by the drug itself through cell-state reprogramming. The dynamics of cell-state transitions both in absence and presence of the drug can be quantified through mathematical models. Such a dynamical systems approach to elucidating patterns of intratumoral heterogeneity by integrating longitudinal experimental data with mathematical models can help design effective combinatorial and/or sequential therapies for better clinical outcomes.
癌症干细胞 (CSC)/非 CSC、药物敏感/耐药状态以及一系列上皮-混合-间充质表型。此外,这些不同的细胞状态可以相互可逆地转换,从而对治疗效果构成重大挑战。因此,了解表型可塑性和异质性的起源仍然是一个活跃的研究领域。虽然已经很好地研究了驱动异质性的基因组成分(突变、染色体不稳定性),但最近的报告强调了非遗传机制在实现表型可塑性和异质性方面的作用。在这里,我们讨论了表型可塑性的各种潜在过程,例如随机基因表达、染色质重编程、不对称细胞分裂和多个稳定基因表达模式(“吸引子”)的存在。这些过程可以促进动态演变的细胞群体,使得(耐药)细胞的亚群能够在致命药物暴露下存活,并在停药后再现群体异质性,导致复发。这些耐药细胞既可以是预先存在的,也可以通过药物本身通过细胞状态重编程诱导产生。在没有和有药物存在的情况下,细胞状态转换的动力学可以通过数学模型进行量化。通过将纵向实验数据与数学模型相结合,以动态系统方法阐明肿瘤内异质性模式,可以帮助设计有效的组合和/或序贯治疗方法,以获得更好的临床结果。