致癌 FGFR4 信号传导是多种癌症类型的潜在治疗靶点，包括三阴性乳腺癌 (TNBC) 和肝细胞癌 (HCC)。然而，对 FGFR4 单药治疗的耐药性仍然是一个重大挑战，强调需要有效的组合治疗。我们的研究旨在开发 FGFR4 信号传导的综合计算模型，并为信号动力学驱动的耐药机制提供网络层面的见解。一种将计算网络模型与实验验证相结合的综合方法，发现了 TNBC 细胞中 FGFR4 靶向后有效的 AKT 重新激活。通过系统模拟模型预测FGFR4与AKT或特定ErbB激酶共靶向的协同作用，分析共靶向特定网络节点的效果，并随后通过实验验证得到证实；然而，共同靶向 FGFR4 和 PI3K 并不具有协同作用。纳入了数百个癌细胞系的蛋白质表达数据，以使模型适应不同的细胞环境。这表明，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，其中共同靶向 FGFR4 和 MEK 但不具有 AKT 的协同作用。总之，这项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模型在设计细胞类型选择性联合疗法和增强精准癌症治疗方面的实用性。

Oncogenic FGFR4 signaling represents a potential therapeutic target in various cancer types, including triple negative breast cancer (TNBC) and hepatocellular carcinoma (HCC). 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 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 TNBC cells. Analyzing the effects of co-targeting specific network nodes by systematically simulating the model predicted synergy of co-targeting FGFR4 and AKT or specific ErbB kinases, which was subsequently confirmed through experimental validation; however, co-targeting 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 while AKT rebound was common, it was not a general phenomenon. For example, ERK reactivation occurred in certain cell types, including an FGFR4-driven HCC cell line, where there is a synergistic effect of co-targeting 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.