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Modeling cell differentiation in neuroblastoma: insights into development, malignancy, and treatment relapse

Abstract:
Neuroblastoma is a paediatric extracranial solid cancer that arises from the developing sympathetic nervous system and is characterised by an abnormal distribution of cell types in tumours compared to healthy infant tissues. In this paper, we propose a new mathematical model of cell differentiation during sympathoadrenal development. By performing Bayesian inference of the model parameters using clinical data from patient samples, we show that the model successfully accounts for the observed differences in cell type heterogeneity among healthy adrenal tissues and four common types of neuroblastomas. Using a phenotypically structured model, we show that alterations in healthy differentiation dynamics are related to cell malignancy, and tumour volume growth. We use this model to analyse the evolution of malignant traits in a tumour. Our findings suggest that normal development dynamics make the embryonic sympathetic nervous system more robust to perturbations and accumulation of malignancies, and that the diversity of differentiation dynamics found in the neuroblastoma subtypes lead to unique risk profiles for neuroblastoma relapse after treatment.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.jtbi.2025.112230

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Role:
Author
ORCID:
0000-0001-8596-8595
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Role:
Author


More from this funder
Funder identifier:
https://ror.org/01cmst727
Grant:
MP-SIP-00001828


Publisher:
Elsevier
Journal:
Journal of Theoretical Biology More from this journal
Volume:
614
Article number:
112230
Publication date:
2025-08-07
Acceptance date:
2025-07-25
DOI:
EISSN:
1095-8541
ISSN:
0022-5193


Language:
English
Pubs id:
2280019
Local pid:
pubs:2280019
Deposit date:
2025-08-12

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