Sung-Young Shin, Nicole J Chew, Milad Ghomlaghi, Anderly C Chüeh, Yunhui Jeong, Lan K Nguyen, Roger J Daly
{"title":"信号网络动态综合建模确定了针对表皮生长因子受体 4(FGFR4)驱动的癌症的细胞类型选择性治疗策略。","authors":"Sung-Young Shin, Nicole J Chew, Milad Ghomlaghi, Anderly C Chüeh, Yunhui Jeong, Lan K Nguyen, Roger J Daly","doi":"10.1158/0008-5472.CAN-23-3409","DOIUrl":null,"url":null,"abstract":"<p><p>Oncogenic FGFR4 signaling represents a potential therapeutic target in various cancer types, including triple-negative breast cancer and hepatocellular carcinoma. However, resistance to FGFR4 single-agent therapy remains a major challenge, emphasizing the need for effective combinatorial treatments. Our study sought to develop a comprehensive computational model of FGFR4 signaling and to provide network-level insights into resistance mechanisms driven by signaling dynamics. An integrated approach, combining computational network modeling with experimental validation, uncovered potent AKT reactivation following FGFR4 targeting in triple-negative breast cancer cells. Analyzing the effects of cotargeting specific network nodes by systematically simulating the model predicted synergy of cotargeting FGFR4 and AKT or specific ErbB kinases, which was subsequently confirmed through experimental validation; however, cotargeting FGFR4 and PI3K was not synergistic. Protein expression data from hundreds of cancer cell lines was incorporated to adapt the model to diverse cellular contexts. This revealed that although AKT rebound was common, it was not a general phenomenon. For example, ERK reactivation occurred in certain cell types, including an FGFR4-driven hepatocellular carcinoma cell line, in which there is a synergistic effect of cotargeting FGFR4 and MEK but not AKT. In summary, this study offers key insights into drug-induced network remodeling and the role of protein expression heterogeneity in targeted therapy responses. These findings underscore the utility of computational network modeling for designing cell type-selective combination therapies and enhancing precision cancer treatment. Significance: Computational predictive modeling of signaling networks can decipher mechanisms of cancer cell resistance to targeted therapies and enable identification of more effective cancer type-specific combination treatment strategies.</p>","PeriodicalId":9441,"journal":{"name":"Cancer research","volume":" ","pages":"3296-3309"},"PeriodicalIF":12.5000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Integrative Modeling of Signaling Network Dynamics Identifies Cell Type-Selective Therapeutic Strategies for FGFR4-Driven Cancers.\",\"authors\":\"Sung-Young Shin, Nicole J Chew, Milad Ghomlaghi, Anderly C Chüeh, Yunhui Jeong, Lan K Nguyen, Roger J Daly\",\"doi\":\"10.1158/0008-5472.CAN-23-3409\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Oncogenic FGFR4 signaling represents a potential therapeutic target in various cancer types, including triple-negative breast cancer and hepatocellular carcinoma. However, resistance to FGFR4 single-agent therapy remains a major challenge, emphasizing the need for effective combinatorial treatments. Our study sought to develop a comprehensive computational model of FGFR4 signaling and to provide network-level insights into resistance mechanisms driven by signaling dynamics. An integrated approach, combining computational network modeling with experimental validation, uncovered potent AKT reactivation following FGFR4 targeting in triple-negative breast cancer cells. Analyzing the effects of cotargeting specific network nodes by systematically simulating the model predicted synergy of cotargeting FGFR4 and AKT or specific ErbB kinases, which was subsequently confirmed through experimental validation; however, cotargeting FGFR4 and PI3K was not synergistic. Protein expression data from hundreds of cancer cell lines was incorporated to adapt the model to diverse cellular contexts. This revealed that although AKT rebound was common, it was not a general phenomenon. For example, ERK reactivation occurred in certain cell types, including an FGFR4-driven hepatocellular carcinoma cell line, in which there is a synergistic effect of cotargeting FGFR4 and MEK but not AKT. In summary, this study offers key insights into drug-induced network remodeling and the role of protein expression heterogeneity in targeted therapy responses. These findings underscore the utility of computational network modeling for designing cell type-selective combination therapies and enhancing precision cancer treatment. Significance: Computational predictive modeling of signaling networks can decipher mechanisms of cancer cell resistance to targeted therapies and enable identification of more effective cancer type-specific combination treatment strategies.</p>\",\"PeriodicalId\":9441,\"journal\":{\"name\":\"Cancer research\",\"volume\":\" \",\"pages\":\"3296-3309\"},\"PeriodicalIF\":12.5000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cancer research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1158/0008-5472.CAN-23-3409\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1158/0008-5472.CAN-23-3409","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
Integrative Modeling of Signaling Network Dynamics Identifies Cell Type-Selective Therapeutic Strategies for FGFR4-Driven Cancers.
Oncogenic FGFR4 signaling represents a potential therapeutic target in various cancer types, including triple-negative breast cancer and hepatocellular carcinoma. However, resistance to FGFR4 single-agent therapy remains a major challenge, emphasizing the need for effective combinatorial treatments. Our study sought to develop a comprehensive computational model of FGFR4 signaling and to provide network-level insights into resistance mechanisms driven by signaling dynamics. An integrated approach, combining computational network modeling with experimental validation, uncovered potent AKT reactivation following FGFR4 targeting in triple-negative breast cancer cells. Analyzing the effects of cotargeting specific network nodes by systematically simulating the model predicted synergy of cotargeting FGFR4 and AKT or specific ErbB kinases, which was subsequently confirmed through experimental validation; however, cotargeting FGFR4 and PI3K was not synergistic. Protein expression data from hundreds of cancer cell lines was incorporated to adapt the model to diverse cellular contexts. This revealed that although AKT rebound was common, it was not a general phenomenon. For example, ERK reactivation occurred in certain cell types, including an FGFR4-driven hepatocellular carcinoma cell line, in which there is a synergistic effect of cotargeting FGFR4 and MEK but not AKT. In summary, this study offers key insights into drug-induced network remodeling and the role of protein expression heterogeneity in targeted therapy responses. These findings underscore the utility of computational network modeling for designing cell type-selective combination therapies and enhancing precision cancer treatment. Significance: Computational predictive modeling of signaling networks can decipher mechanisms of cancer cell resistance to targeted therapies and enable identification of more effective cancer type-specific combination treatment strategies.
期刊介绍:
Cancer Research, published by the American Association for Cancer Research (AACR), is a journal that focuses on impactful original studies, reviews, and opinion pieces relevant to the broad cancer research community. Manuscripts that present conceptual or technological advances leading to insights into cancer biology are particularly sought after. The journal also places emphasis on convergence science, which involves bridging multiple distinct areas of cancer research.
With primary subsections including Cancer Biology, Cancer Immunology, Cancer Metabolism and Molecular Mechanisms, Translational Cancer Biology, Cancer Landscapes, and Convergence Science, Cancer Research has a comprehensive scope. It is published twice a month and has one volume per year, with a print ISSN of 0008-5472 and an online ISSN of 1538-7445.
Cancer Research is abstracted and/or indexed in various databases and platforms, including BIOSIS Previews (R) Database, MEDLINE, Current Contents/Life Sciences, Current Contents/Clinical Medicine, Science Citation Index, Scopus, and Web of Science.