Mechanistic models position ceritinib as a nuclear integrity disrupting therapy in pediatric liver tumors
Salih Demir1, Thomas Kessler2, Alina Hotes1
1Department of Pediatric Surgery, Dr. Von Hauner Children's Hospital, LMU University Hospital, LMU Munich, Lindwurmstr. 2a, Munich, 80337, Germany.
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.
Insights
Computational models identified ceritinib as a promising treatment for high-risk pediatric liver tumors. This drug targets key kinases, disrupts nuclear integrity, and shows efficacy in preclinical models.
Area of Science:
- Computational biology and oncology
- Translational medicine
- Drug discovery
Background:
- High-risk pediatric liver tumors present significant therapeutic challenges.
- Targeted treatment strategies are crucial for improving patient outcomes.
- Computational modeling offers a novel approach to identify effective therapies.
Purpose of the Study:
- To leverage patient-specific mechanistic cell models to identify alternative treatments for pediatric liver tumors.
- To utilize computational drug response simulations for predicting therapeutic options.
Main Methods:
- Generated digital twins of pediatric liver tumor patients using clinical, genetic, and transcriptomic data.
- Performed in silico drug response simulations with mechanistic models.
- Validated the efficacy of ceritinib in vitro and in vivo using patient-derived xenograft models.
Main Results:
- Mechanistic models identified ceritinib as the most effective treatment via in silico simulations.
- Ceritinib inhibited tumor growth by targeting non-canonical kinases, unlike other ALK inhibitors.
- Ceritinib suppressed nucleoporin expression, disrupted nuclear integrity, and induced apoptosis, reducing tumor burden in mouse models.
Conclusions:
- Mechanistic models applied to virtual patients successfully positioned ceritinib as a promising therapeutic agent for high-risk pediatric liver tumors.
- Ceritinib demonstrates efficacy by targeting kinases involved in tumor aggressiveness and compromising nuclear integrity.
- This approach highlights the potential of computational modeling in accelerating drug discovery for challenging pediatric cancers.
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