肿瘤由不同的基因型亚种或亚克隆细胞组成。这些亚克隆可以通过称为“克隆相互作用”的过程影响相邻的克隆。传统上，对于癌症驱动基因突变的研究侧重于它们对含有驱动基因的细胞健康的细胞内效应。最近，随着对肿瘤异质性和克隆动力学进行研究的实验和计算技术的进步，新的研究表明克隆相互作用在癌症的发生、发展和转移中的重要性。在本篇评论中，我们提供了癌症克隆相互作用的概述，讨论来自癌症生物学研究不同方法的关键发现。我们讨论了常见的克隆相互作用类型，例如合作和竞争，其机制以及对肿瘤发生的总体影响，具有肿瘤异质性、治疗抵抗和肿瘤抑制等重要意义。数量模型-与细胞培养和动物模型实验协调使用-在研究克隆相互作用的性质和它们产生的复杂克隆动力学方面发挥了重要作用。我们提供用于表示克隆相互作用的数学和计算模型，并提供了它们在实验系统中识别和量化克隆相互作用强度方面所扮演的角色的例子。临床数据中很难观察到克隆相互作用；然而，几种最新的定量方法可以检测到它们。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发挥的关键-并且往往令人惊讶的-作用。Copyright © 2023 Elsevier Ltd. All rights reserved.

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.Copyright © 2023 Elsevier Ltd. All rights reserved.