Jincan Che , Yu Wang , Li Feng , Claudia Gragnoli , Christopher Griffin , Rongling Wu
{"title":"肿瘤生长中肿瘤-微环境串扰的高阶相互作用模型","authors":"Jincan Che , Yu Wang , Li Feng , Claudia Gragnoli , Christopher Griffin , Rongling Wu","doi":"10.1016/j.plrev.2025.05.007","DOIUrl":null,"url":null,"abstract":"<div><div>Signaling interactions between cancer cells and nonmalignant cells in the tumor microenvironment (TME) are believed to influence tumor progression and drug resistance. However, the genomic machineries mediating such an influence remain elusive, making it difficult to determine therapeutic targets on the tumor and its microenvironment. Here, we argue that a computational model, derived from the integration of evolutionary game theory and ecosystem theory through allometric scaling law, can chart the genomic atlas of high-order interaction networks involving tumor cells, TME, and tumor mass. We assess the application of this model to identify the causal influence of gene-induced tumor-TME crosstalk on tumor growth. The findings demonstrate that cooperation and competition between tumor cells and their infiltrating microenvironment promote or inhibit tumor growth in diverse ways. We identify specific genes that govern this promotion or inhibition, which can be used as genetic targets to alter tumor growth. This model opens up a new avenue to precisely infer the genomic underpinnings of tumor-TME interactions and their impact on tumor progression from any omics data.</div></div>","PeriodicalId":403,"journal":{"name":"Physics of Life Reviews","volume":"54 ","pages":"Pages 11-23"},"PeriodicalIF":14.3000,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"High-order interaction modeling of tumor-microenvironment crosstalk for tumor growth\",\"authors\":\"Jincan Che , Yu Wang , Li Feng , Claudia Gragnoli , Christopher Griffin , Rongling Wu\",\"doi\":\"10.1016/j.plrev.2025.05.007\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Signaling interactions between cancer cells and nonmalignant cells in the tumor microenvironment (TME) are believed to influence tumor progression and drug resistance. However, the genomic machineries mediating such an influence remain elusive, making it difficult to determine therapeutic targets on the tumor and its microenvironment. Here, we argue that a computational model, derived from the integration of evolutionary game theory and ecosystem theory through allometric scaling law, can chart the genomic atlas of high-order interaction networks involving tumor cells, TME, and tumor mass. We assess the application of this model to identify the causal influence of gene-induced tumor-TME crosstalk on tumor growth. The findings demonstrate that cooperation and competition between tumor cells and their infiltrating microenvironment promote or inhibit tumor growth in diverse ways. We identify specific genes that govern this promotion or inhibition, which can be used as genetic targets to alter tumor growth. This model opens up a new avenue to precisely infer the genomic underpinnings of tumor-TME interactions and their impact on tumor progression from any omics data.</div></div>\",\"PeriodicalId\":403,\"journal\":{\"name\":\"Physics of Life Reviews\",\"volume\":\"54 \",\"pages\":\"Pages 11-23\"},\"PeriodicalIF\":14.3000,\"publicationDate\":\"2025-05-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Physics of Life Reviews\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1571064525000855\",\"RegionNum\":1,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physics of Life Reviews","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1571064525000855","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOLOGY","Score":null,"Total":0}
High-order interaction modeling of tumor-microenvironment crosstalk for tumor growth
Signaling interactions between cancer cells and nonmalignant cells in the tumor microenvironment (TME) are believed to influence tumor progression and drug resistance. However, the genomic machineries mediating such an influence remain elusive, making it difficult to determine therapeutic targets on the tumor and its microenvironment. Here, we argue that a computational model, derived from the integration of evolutionary game theory and ecosystem theory through allometric scaling law, can chart the genomic atlas of high-order interaction networks involving tumor cells, TME, and tumor mass. We assess the application of this model to identify the causal influence of gene-induced tumor-TME crosstalk on tumor growth. The findings demonstrate that cooperation and competition between tumor cells and their infiltrating microenvironment promote or inhibit tumor growth in diverse ways. We identify specific genes that govern this promotion or inhibition, which can be used as genetic targets to alter tumor growth. This model opens up a new avenue to precisely infer the genomic underpinnings of tumor-TME interactions and their impact on tumor progression from any omics data.
期刊介绍:
Physics of Life Reviews, published quarterly, is an international journal dedicated to review articles on the physics of living systems, complex phenomena in biological systems, and related fields including artificial life, robotics, mathematical bio-semiotics, and artificial intelligent systems. Serving as a unifying force across disciplines, the journal explores living systems comprehensively—from molecules to populations, genetics to mind, and artificial systems modeling these phenomena. Inviting reviews from actively engaged researchers, the journal seeks broad, critical, and accessible contributions that address recent progress and sometimes controversial accounts in the field.