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。

IF 12.5 1区 医学 Q1 ONCOLOGY Cancer research Pub Date : 2024-10-01 DOI:10.1158/0008-5472.CAN-23-3409
Sung-Young Shin, Nicole J Chew, Milad Ghomlaghi, Anderly C Chüeh, Yunhui Jeong, Lan K Nguyen, Roger J Daly
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引用次数: 0

摘要

致癌基因 FGFR4 信号是包括三阴性乳腺癌(TNBC)和肝细胞癌(HCC)在内的各种癌症的潜在治疗靶点。然而,对 FGFR4 单药治疗的耐药性仍然是一个重大挑战,这强调了对有效组合疗法的需求。我们的研究试图建立一个全面的 FGFR4 信号传导计算模型,并从网络层面深入了解信号传导动态驱动的耐药机制。通过将计算网络建模与实验验证相结合的综合方法,我们发现了FGFR4靶向TNBC细胞后AKT的强效再激活。通过系统模拟模型来分析共同靶向特定网络节点的效果,预测了共同靶向FGFR4和AKT或特定ErbB激酶的协同作用,随后通过实验验证证实了这一点;然而,共同靶向FGFR4和PI3K并没有协同作用。为了使模型适应不同的细胞环境,我们纳入了数百种癌细胞系的蛋白质表达数据。结果发现,虽然 AKT 反弹很常见,但并不是普遍现象。例如,ERK重新激活发生在某些细胞类型中,包括FGFR4驱动的HCC细胞系,在这种细胞系中,联合靶向FGFR4和MEK会产生协同效应,但AKT不会。总之,本研究为药物诱导的网络重塑以及蛋白质表达异质性在靶向治疗反应中的作用提供了重要见解。这些发现强调了计算网络建模在设计细胞类型选择性联合疗法和提高癌症精准治疗方面的实用性。
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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.

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来源期刊
Cancer research
Cancer research 医学-肿瘤学
CiteScore
16.10
自引率
0.90%
发文量
7677
审稿时长
2.5 months
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.
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