Hepatocellular carcinoma (HCC) is a complex and deadly disease lacking druggable genetic mutations. The limited efficacy of systemic treatments for advanced HCC implies that predictive biomarkers and drug targets are urgently needed. Most HCC drugs target protein kinases, indicating that kinase-dependent signaling networks drive HCC progression. To identify HCC signaling networks that determine responses to kinase inhibitors (KIs), we apply a pharmacoproteomics approach integrating kinome activity in 17 HCC cell lines with their responses to 299 KIs, resulting in a comprehensive dataset of pathway-based drug response signatures. By profiling patient HCC samples, we identify signatures of clinical HCC drug responses in individual tumors. Our analyses reveal kinase networks promoting the epithelial-mesenchymal transition (EMT) and drug resistance, including a FZD2-AXL-NUAK1/2 signaling module, whose inhibition reverses the EMT and sensitizes HCC cells to drugs. Our approach identifies cancer drug targets and molecular signatures of drug response for personalized oncology.

肝细胞癌 (HCC) 是一种复杂且致命的疾病，缺乏可成药的基因突变。晚期 HCC 全身治疗的有限疗效意味着迫切需要预测性生物标志物和药物靶点。大多数 HCC 药物靶向蛋白激酶，表明激酶依赖性信号网络驱动 HCC 进展。为了确定决定对激酶抑制剂 (KI) 反应的 HCC 信号网络，我们应用了一种药物蛋白质组学方法，将 17 个 HCC 细胞系中的激酶组活性与其对 299 个 KI 的反应相结合，从而产生了基于途径的药物反应特征的综合数据集。通过分析患者 HCC 样本，我们确定了个体肿瘤中临床 HCC 药物反应的特征。我们的分析揭示了促进上皮间质转化 (EMT) 和耐药性的激酶网络，包括 FZD2-AXL-NUAK1/2 信号模块，其抑制逆转 EMT 并使 HCC 细胞对药物敏感。我们的方法为个性化肿瘤学确定了癌症药物靶点和药物反应的分子特征。