Salih Demir, Thomas Kessler, Alina Hotes, Beate Häberle, Eiso Hiyama, Tomoro Hishiki, Emilie Indersie, Sophie Branchereau, Christian Vokuhl, Mathurin Dorel, Hans Lehrach, Bodo Lange, Stefano Cairo, Roland Kappler
{"title":"机制模型定位西瑞替尼作为儿童肝肿瘤的核完整性破坏疗法。","authors":"Salih Demir, Thomas Kessler, Alina Hotes, Beate Häberle, Eiso Hiyama, Tomoro Hishiki, Emilie Indersie, Sophie Branchereau, Christian Vokuhl, Mathurin Dorel, Hans Lehrach, Bodo Lange, Stefano Cairo, Roland Kappler","doi":"10.1186/s13046-025-03535-z","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":50199,"journal":{"name":"Journal of Experimental & Clinical Cancer Research","volume":"44 1","pages":"268"},"PeriodicalIF":12.8000,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12445023/pdf/","citationCount":"0","resultStr":"{\"title\":\"Mechanistic models position ceritinib as a nuclear integrity disrupting therapy in pediatric liver tumors.\",\"authors\":\"Salih Demir, Thomas Kessler, Alina Hotes, Beate Häberle, Eiso Hiyama, Tomoro Hishiki, Emilie Indersie, Sophie Branchereau, Christian Vokuhl, Mathurin Dorel, Hans Lehrach, Bodo Lange, Stefano Cairo, Roland Kappler\",\"doi\":\"10.1186/s13046-025-03535-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>\",\"PeriodicalId\":50199,\"journal\":{\"name\":\"Journal of Experimental & Clinical Cancer Research\",\"volume\":\"44 1\",\"pages\":\"268\"},\"PeriodicalIF\":12.8000,\"publicationDate\":\"2025-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12445023/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Experimental & Clinical Cancer Research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s13046-025-03535-z\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Experimental & Clinical Cancer Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s13046-025-03535-z","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
Mechanistic models position ceritinib as a nuclear integrity disrupting therapy in pediatric liver tumors.
Background: 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.
Methods: 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.
Results: 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.
Conclusion: 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.
期刊介绍:
The Journal of Experimental & Clinical Cancer Research is an esteemed peer-reviewed publication that focuses on cancer research, encompassing everything from fundamental discoveries to practical applications.
We welcome submissions that showcase groundbreaking advancements in the field of cancer research, especially those that bridge the gap between laboratory findings and clinical implementation. Our goal is to foster a deeper understanding of cancer, improve prevention and detection strategies, facilitate accurate diagnosis, and enhance treatment options.
We are particularly interested in manuscripts that shed light on the mechanisms behind the development and progression of cancer, including metastasis. Additionally, we encourage submissions that explore molecular alterations or biomarkers that can help predict the efficacy of different treatments or identify drug resistance. Translational research related to targeted therapies, personalized medicine, tumor immunotherapy, and innovative approaches applicable to clinical investigations are also of great interest to us.
We provide a platform for the dissemination of large-scale molecular characterizations of human tumors and encourage researchers to share their insights, discoveries, and methodologies with the wider scientific community.
By publishing high-quality research articles, reviews, and commentaries, the Journal of Experimental & Clinical Cancer Research strives to contribute to the continuous improvement of cancer care and make a meaningful impact on patients' lives.