Hepatocellular carcinoma (HCC) is the most common form of primary adult liver cancer. After nearly a decade with sorafenib as the only approved treatment, multiple new agents have demonstrated efficacy in clinical trials, including the targeted therapies regorafenib, lenvatinib and cabozantinib, the anti-angiogenic antibody ramucirumab, and the immune checkpoint inhibitors nivolumab and pembrolizumab. Although these agents offer new promise to patients with HCC, the optimal choice and sequence of therapies remains unknown and without established biomarkers, and many patients do not respond to treatment. The advances and the decreasing costs of molecular measurement technologies enable profiling of HCC molecular features (such as genome, transcriptome, proteome and metabolome) at different levels, including bulk tissues, animal models and single cells. The release of such data sets to the public enhances the ability to search for information from these legacy studies and provides the opportunity to leverage them to understand HCC mechanisms, rationally develop new therapeutics and identify candidate biomarkers of treatment response. Here, we provide a comprehensive review of public data sets related to HCC and discuss how emerging artificial intelligence methods can be applied to identify new targets and drugs as well as to guide therapeutic choices for improved HCC treatment.

中文翻译：

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利用大“组学”数据和人工智能进行肝细胞癌药物发现。

肝细胞癌 (HCC) 是最常见的原发性成人肝癌。在索拉非尼作为唯一获批的治疗药物近十年后，多种新药已在临床试验中显示出疗效，包括靶向治疗瑞格非尼、乐伐替尼和卡博替尼、抗血管生成抗体雷莫芦单抗，以及免疫检查点抑制剂纳武单抗和派姆单抗。尽管这些药物为 HCC 患者带来了新的希望，但治疗的最佳选择和顺序仍然未知，并且没有确定的生物标志物，而且许多患者对治疗没有反应。分子测量技术的进步和成本的降低使得能够在不同水平上分析 HCC 分子特征（如基因组、转录组、蛋白质组和代谢组），包括大块组织、动物模型和单细胞。向公众发布此类数据集增强了从这些传统研究中搜索信息的能力，并提供了利用它们了解 HCC 机制、合理开发新疗法和确定治疗反应的候选生物标志物的机会。在这里，我们对与 HCC 相关的公共数据集进行了全面审查，并讨论了如何应用新兴的人工智能方法来识别新的靶点和药物，以及指导治疗选择以改善 HCC 治疗。