{"title":"Cancer mutationscape: revealing the link between modular restructuring and intervention efficacy among mutations.","authors":"Daniel Plaugher, David Murrugarra","doi":"10.1038/s41540-024-00398-6","DOIUrl":null,"url":null,"abstract":"<p><p>There is increasing evidence that biological systems are modular in both structure and function. Complex biological signaling networks such as gene regulatory networks (GRNs) are proving to be composed of subcategories that are interconnected and hierarchically ranked. These networks contain highly dynamic processes that ultimately dictate cellular function over time, as well as influence phenotypic fate transitions. In this work, we use a stochastic multicellular signaling network of pancreatic cancer (PC) to show that the variance in topological rankings of the most phenotypically influential modules implies a strong relationship between structure and function. We further show that induction of mutations alters the modular structure, which analogously influences the aggression and controllability of the disease in silico. We finally present evidence that the impact and location of mutations with respect to PC modular structure directly corresponds to the efficacy of single agent treatments in silico, because topologically deep mutations require deep targets for control.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":null,"pages":null},"PeriodicalIF":3.5000,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11246485/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"NPJ Systems Biology and Applications","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1038/s41540-024-00398-6","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
There is increasing evidence that biological systems are modular in both structure and function. Complex biological signaling networks such as gene regulatory networks (GRNs) are proving to be composed of subcategories that are interconnected and hierarchically ranked. These networks contain highly dynamic processes that ultimately dictate cellular function over time, as well as influence phenotypic fate transitions. In this work, we use a stochastic multicellular signaling network of pancreatic cancer (PC) to show that the variance in topological rankings of the most phenotypically influential modules implies a strong relationship between structure and function. We further show that induction of mutations alters the modular structure, which analogously influences the aggression and controllability of the disease in silico. We finally present evidence that the impact and location of mutations with respect to PC modular structure directly corresponds to the efficacy of single agent treatments in silico, because topologically deep mutations require deep targets for control.
越来越多的证据表明,生物系统的结构和功能都是模块化的。事实证明,基因调控网络(GRN)等复杂的生物信号网络是由相互关联和分级的子类别组成的。这些网络包含高度动态的过程,随着时间的推移最终决定细胞的功能,并影响表型的命运转变。在这项研究中,我们利用胰腺癌(PC)的随机多细胞信号网络来证明,对表型影响最大的模块的拓扑排名差异意味着结构与功能之间存在密切关系。我们进一步证明,诱导突变会改变模块结构,而模块结构又会影响疾病的侵袭性和可控性。我们最后提出的证据表明,突变对 PC 模块结构的影响和位置直接对应于硅学中单药治疗的疗效,因为拓扑学上的深度突变需要深度控制目标。
期刊介绍:
npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology.
We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.