Demir Salih, Kessler Thomas, Hotes Alina, Häberle Beate, Hiyama Eiso, Hishiki Tomoro, Indersie Emilie, Branchereau Sophie, Vokuhl Christian, Dorel Mathurin, Lehrach Hans, Lange Bodo, Cairo Stefano, Kappler Roland
Department of Pediatric Surgery, Dr. Von Hauner Children's Hospital, LMU University Hospital, LMU Munich, Lindwurmstr. 2a, Munich, 80337, Germany.
Alacris Theranostics, Berlin, Germany.
J Exp Clin Cancer Res. 2025 Sep 18;44(1):268. doi: 10.1186/s13046-025-03535-z.
Pediatric liver tumors with high-risk features pose therapeutic challenges, necessitating the development of more targeted and effective treatment strategies. Computational modeling of virtual patients and in silico drug response simulations, based on properly trained mechanistic models, is a powerful strategy to predict new treatment options. We aimed to leverage patient-specific mechanistic cell models to identify therapeutic alternatives for pediatric patients with high-risk liver tumors.
We generated digital twins of high-risk pediatric liver tumor patients by integrating clinical, genetic, and transcriptomic data and performed computational drug response simulations using mechanistic models. We validated the therapeutic potential of ceritinib in patient-derived xenograft models both in vitro and in vivo and used fluorescence microscopy-based imaging for functional analyses.
Mechanistic models trained with digital twins of high-risk pediatric liver tumor patients identified ceritinib as the most effective treatment option through iterated in silico drug response simulations. Validation on a comprehensive drug-testing platform demonstrated that ceritinib, unlike other ALK receptor tyrosine kinase inhibitors with lower prediction scores, inhibited tumor growth by targeting non-canonical kinases. Mechanistically, ceritinib suppressed expression of nucleoporins, essential components of the nuclear pore complex overexpressed in pediatric liver tumors, consequently leading to the disruption of nuclear membrane integrity, perinuclear accumulation of mitochondria, production of reactive oxygen species, and induction of apoptosis. In patient-derived xenograft mouse models, ceritinib reduced tumor burden and extended survival by promoting cell death.
This study demonstrates the successful application of mechanistic models on virtual patients to position ceritinib as a promising therapeutic agent for high-risk pediatric liver tumors, highlighting its impact on key kinases implicated in tumor aggressiveness and its ability to compromise nuclear integrity.
具有高危特征的小儿肝肿瘤带来了治疗挑战，因此需要开发更具针对性和有效性的治疗策略。基于经过适当训练的机制模型对虚拟患者进行计算建模和计算机药物反应模拟，是预测新治疗方案的有力策略。我们旨在利用患者特异性机制细胞模型来确定高危小儿肝肿瘤患者的治疗替代方案。
我们通过整合临床、遗传和转录组数据生成了高危小儿肝肿瘤患者的数字孪生模型，并使用机制模型进行了计算药物反应模拟。我们在体外和体内的患者来源异种移植模型中验证了色瑞替尼的治疗潜力，并使用基于荧光显微镜的成像进行功能分析。
通过对高危小儿肝肿瘤患者数字孪生模型进行训练的机制模型，经反复的计算机药物反应模拟，确定色瑞替尼为最有效的治疗选择。在一个综合药物测试平台上的验证表明，与其他预测分数较低的ALK受体酪氨酸激酶抑制剂不同，色瑞替尼通过靶向非经典激酶抑制肿瘤生长。从机制上讲，色瑞替尼抑制了核孔蛋白的表达，核孔蛋白是小儿肝肿瘤中过度表达的核孔复合体的重要组成部分，从而导致核膜完整性破坏、线粒体核周积聚、活性氧产生及凋亡诱导。在患者来源的异种移植小鼠模型中，色瑞替尼通过促进细胞死亡减轻了肿瘤负担并延长了生存期。
本研究证明了机制模型在虚拟患者中的成功应用，将色瑞替尼定位为高危小儿肝肿瘤的一种有前景的治疗药物，突出了其对与肿瘤侵袭性相关的关键激酶的影响以及破坏核完整性的能力。
