Department of Applied Mathematics, University of Washington, Seattle, WA, United States.
Department of Applied Mathematics, University of Washington, Seattle, WA, United States; Herbold Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, United States.
Semin Cancer Biol. 2023 Jul;92:61-73. doi: 10.1016/j.semcancer.2023.04.002. Epub 2023 Apr 5.
Tumors consist of different genotypically distinct subpopulations-or subclones-of cells. These subclones can influence neighboring clones in a process called "clonal interaction." Conventionally, research on driver mutations in cancer has focused on their cell-autonomous effects that lead to an increase in fitness of the cells containing the driver. Recently, with the advent of improved experimental and computational technologies for investigating tumor heterogeneity and clonal dynamics, new studies have shown the importance of clonal interactions in cancer initiation, progression, and metastasis. In this review we provide an overview of clonal interactions in cancer, discussing key discoveries from a diverse range of approaches to cancer biology research. We discuss common types of clonal interactions, such as cooperation and competition, its mechanisms, and the overall effect on tumorigenesis, with important implications for tumor heterogeneity, resistance to treatment, and tumor suppression. Quantitative models-in coordination with cell culture and animal model experiments-have played a vital role in investigating the nature of clonal interactions and the complex clonal dynamics they generate. We present mathematical and computational models that can be used to represent clonal interactions and provide examples of the roles they have played in identifying and quantifying the strength of clonal interactions in experimental systems. Clonal interactions have proved difficult to observe in clinical data; however, several very recent quantitative approaches enable their detection. We conclude by discussing ways in which researchers can further integrate quantitative methods with experimental and clinical data to elucidate the critical-and often surprising-roles of clonal interactions in human cancers.
肿瘤由不同基因型不同的亚群或细胞亚克隆组成。这些亚克隆可以通过一种称为“克隆相互作用”的过程影响邻近的克隆。传统上，癌症驱动突变的研究集中在其细胞自主效应上，这些效应导致含有驱动突变的细胞适应性增加。最近，随着用于研究肿瘤异质性和克隆动力学的改进实验和计算技术的出现，新的研究表明克隆相互作用在癌症的起始、进展和转移中具有重要意义。在这篇综述中，我们提供了癌症中克隆相互作用的概述，讨论了来自癌症生物学研究的各种方法的关键发现。我们讨论了常见的克隆相互作用类型，如合作和竞争，它们的机制，以及对肿瘤发生的总体影响，这对肿瘤异质性、治疗耐药性和肿瘤抑制具有重要意义。定量模型——与细胞培养和动物模型实验相结合——在研究克隆相互作用的性质及其产生的复杂克隆动力学方面发挥了至关重要的作用。我们提出了可以用来表示克隆相互作用的数学和计算模型，并提供了它们在识别和量化实验系统中克隆相互作用强度方面所起作用的例子。克隆相互作用在临床数据中很难观察到；然而，最近有几种非常定量的方法能够检测到它们。最后，我们讨论了研究人员如何进一步将定量方法与实验和临床数据相结合，以阐明克隆相互作用在人类癌症中的关键作用——通常是令人惊讶的作用。
