肝母细胞瘤（HB）是儿童主要的肝脏癌症，但这是一种非常罕见的疾病。尽管在HB患儿的治疗方面取得了显著进展，但对于晚期肿瘤患者来说，目前的治疗选择有限。此外，幸存者通常会出现由治疗引起的长期不良反应，如耳毒性、心脏毒性、生长迟缓和二次肿瘤。因此，迫切需要为HB患者定义新的有效治疗策略。虽然计算方法可以成功预测某些常见成人恶性肿瘤的药物敏感性，但由于数据匮乏，儿科肿瘤领域缺乏相关研究。在本研究中，我们基于转录组数据对具有侵袭性C2亚型和不良临床结局的HB患者的药物疗效进行了计算筛选。我们的方法利用了公开可得的跨36种肿瘤类型和495种化合物的全癌症转录谱和药物反应数据集。经过实验证实，我们预测的最有效药物使用了在体外和体内培养的HB患者来源的异种移植瘤（PDX）模型。因此，我们鉴定出两种CDK9抑制剂，alvocidib和dinaciclib，作为高风险C2分子亚型的强效HB生长抑制剂。我们还发现，在46名HB患者队列中，高CDK9肿瘤表达与不良预后显著相关。我们的工作证明了在如HB等罕见儿科肿瘤中，基于全癌症数据集的计算方法在药物再定位方面的实用性，以及为临床医生选择最佳治疗方案提供帮助。版权所有 © 2023 作者。Wolters Kluwer Health, Inc.发表。

Hepatoblastoma (HB) is the main paediatric liver cancer, but it is a very rare disease. Despite significant improvements in the treatment of children diagnosed with HB, limited treatment options exist for patients with advanced tumours. Besides, survivors generally have long-term adverse effects derived from treatment such as ototoxicity, cardiotoxicity, delayed growth, and secondary tumours. Accordingly, there is an urgent need to define new and efficient therapeutic strategies for patients with HB. Computational methods to predict drug sensitivity from a tumour's transcriptome have been successfully applied for some common adult malignancies, but specific efforts in paediatric cancers are lacking because of paucity of data. In this study, we computationally screened the efficacy of drugs in HB patients with the aggressive C2 subtype and poor clinical outcome starting from their transcriptome. Our method utilized publicly available collections of pan-cancer transcriptional profiles and drug responses across 36 tumour types and 495 compounds. The drugs predicted to be most effective were experimentally validated using patient-derived xenograft (PDX) models of HB grown in vitro and in vivo. We thus identified two CDK9 inhibitors, alvocidib and dinaciclib as potent HB growth inhibitors for the high-risk C2 molecular subtype. We also found that in a cohort of 46 patients with HB, high CDK9 tumour expression was significantly associated with poor prognosis. Our work proves the usefulness of computational methods trained on pan-cancer datasets to reposition drugs in rare paediatric cancers such as HB, and to help clinicians in choosing the best treatment options for their patients.Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.