Evidence map›Paper›PMID 40968384›Full record

ArticleJournal of experimental & clinical cancer research : CR2025

Mechanistic models position ceritinib as a nuclear integrity disrupting therapy in pediatric liver tumors.

Salih Demir, Thomas Kessler, Alina Hotes, Beate Häberle, Eiso Hiyama, Tomoro Hishiki, Emilie Indersie, Sophie Branchereau, Christian Vokuhl, Mathurin Dorel and 4 more

Abstract read
In one paragraph

Article in Journal of experimental & clinical cancer research : CR, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Salih DemirDepartment of Pediatric Surgery, Dr. Von Hauner Children's Hospital, LMU University Hospital, LMU Munich, Lindwurmstr. 2a, Munich, 80337, Germany.
Thomas KesslerAlacris Theranostics, Berlin, Germany.
Alina HotesDepartment of Pediatric Surgery, Dr. Von Hauner Children's Hospital, LMU University Hospital, LMU Munich, Lindwurmstr. 2a, Munich, 80337, Germany.
Beate HäberleDepartment of Pediatric Surgery, Dr. Von Hauner Children's Hospital, LMU University Hospital, LMU Munich, Lindwurmstr. 2a, Munich, 80337, Germany.
Eiso HiyamaNatural Science Center for Basic Research and Development, Hiroshima University, Hiroshima, Japan.
Tomoro HishikiDepartment of Pediatric Surgery, Chiba University Graduate School of Medicine, Chiba, Japan.
Emilie IndersieXenTech, Evry, France.
Sophie BranchereauBicêtre Hospital, AP-HP Paris Saclay University, Paris, France.
Christian VokuhlInstitute of Pathology, University Hospital Bonn, Bonn, Germany.
Mathurin DorelMax-Planck-Institute for Molecular Genetics, Berlin, Germany.
Hans LehrachMax-Planck-Institute for Molecular Genetics, Berlin, Germany.
Bodo LangeAlacris Theranostics, Berlin, Germany.
Stefano CairoChampions Oncology, Inc., Rockville, MD, USA.
Roland KapplerDepartment of Pediatric Surgery, Dr. Von Hauner Children's Hospital, LMU University Hospital, LMU Munich, Lindwurmstr. 2a, Munich, 80337, Germany. roland.kappler@med.uni-muenchen.de.

Funding

Horizon 2020 668596 (ChiLTERN)Horizon 2020,European Union 826121 (iPC)
6 · The paper itself

Abstract

backgroundPediatric 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.

methodsWe 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.

resultsMechanistic 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.

conclusionThis 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.

Indexed as

Antineoplastic AgentsCell NucleusLiver NeoplasmsProtein Kinase InhibitorsPyrimidinesSulfonesAnimalsCell Line, TumorChildFemaleHumansMaleMiceXenograft Model Antitumor AssaysAntineoplastic AgentsceritinibProtein Kinase InhibitorsPyrimidinesSulfonesCeritinibMechanistic modelsNuclear pore complexPediatric liver cancerTherapeutic strategy

Identifiers

PMID40968384
PMCPMC12445023

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.