Models of Cell Signaling Uncover Molecular Mechanisms of High-risk Neuroblastoma and Predict Disease Outcome

Overview

Journal Biol Direct

Publisher Biomed Central

Specialty Biology

Date 2018 Aug 24

PMID 30134948

Citations 15

Authors

Marta R Hidalgo

Alicia Amadoz

Cankut Cubuk

Jose Carbonell-Caballero

Joaquin Dopazo

Affiliations

Soon will be listed here.

Abstract

Background: Despite the progress in neuroblastoma therapies the mortality of high-risk patients is still high (40-50%) and the molecular basis of the disease remains poorly known. Recently, a mathematical model was used to demonstrate that the network regulating stress signaling by the c-Jun N-terminal kinase pathway played a crucial role in survival of patients with neuroblastoma irrespective of their MYCN amplification status. This demonstrates the enormous potential of computational models of biological modules for the discovery of underlying molecular mechanisms of diseases.

Results: Since signaling is known to be highly relevant in cancer, we have used a computational model of the whole cell signaling network to understand the molecular determinants of bad prognostic in neuroblastoma. Our model produced a comprehensive view of the molecular mechanisms of neuroblastoma tumorigenesis and progression.

Conclusion: We have also shown how the activity of signaling circuits can be considered a reliable model-based prognostic biomarker.

Reviewers: This article was reviewed by Tim Beissbarth, Wenzhong Xiao and Joanna Polanska. For the full reviews, please go to the Reviewers' comments section.

Citing Articles

Prediction of Composite Clinical Outcomes for Childhood Neuroblastoma Using Multi-Omics Data and Machine Learning.

Wang P, Zhang J Int J Mol Sci. 2025; 26(1.

PMID: 39795994 PMC: 11720239. DOI: 10.3390/ijms26010136.

Multicellular model of neuroblastoma proposes unconventional therapy based on multiple roles of p53.

Wertheim K, Chisholm R, Richmond P, Walker D PLoS Comput Biol. 2024; 20(12):e1012648.

PMID: 39715281 PMC: 11723635. DOI: 10.1371/journal.pcbi.1012648.

The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery.

Esteban-Medina M, Loucera C, Rian K, Velasco S, Olivares-Gonzalez L, Rodrigo R J Transl Med. 2024; 22(1):139.

PMID: 38321543 PMC: 10848380. DOI: 10.1186/s12967-024-04911-7.

Crosstalk between Metabolite Production and Signaling Activity in Breast Cancer.

Cubuk C, Loucera C, Pena-Chilet M, Dopazo J Int J Mol Sci. 2023; 24(8).

PMID: 37108611 PMC: 10138666. DOI: 10.3390/ijms24087450.

Mathematical Model of Clonal Evolution Proposes a Personalised Multi-Modal Therapy for High-Risk Neuroblastoma.

Italia M, Wertheim K, Taschner-Mandl S, Walker D, Dercole F Cancers (Basel). 2023; 15(7).

PMID: 37046647 PMC: 10093626. DOI: 10.3390/cancers15071986.

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