Models of cell signaling uncover molecular mechanisms of high-risk neuroblastoma and predict disease outcome

Abstract

BackgroundDespite 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.ResultsSince 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.ConclusionWe have also shown how the activity of signaling circuits can be considered a reliable model-based prognostic biomarker.ReviewersThis article was reviewed by Tim Beissbarth, Wenzhong Xiao and Joanna Polanska. For the full reviews, please go to the Reviewers’ comments section.Electronic supplementary materialThe online version of this article (10.1186/s13062-018-0219-4) contains supplementary material, which is available to authorized users.

Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models

“…To achieve so, we have used signaling circuit activities inferred by mechanistic models, as proxies of disease-related cell functionalities triggered by them. Such mechanistic models use gene expression data to produce an estimation of profiles of signaling or metabolic circuit activity within pathways [20,24] and have been used to describe the molecular mechanisms behind different biological scenarios such as the explanation on how stressinduced activation of brown adipose tissue prevents obesity [25], the common molecular mechanisms of three cancer-prone genodermatoses [49] or the molecular mechanisms of death and the postmortem the ischemia of a tissue [26]. Moreover, recent benchmarking of mechanistic modeling methods shows how Hipathia clearly outperform to other competing method [50].…”

Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models

“…To achieve so, we have used signaling circuit activities inferred by mechanistic models, as proxies of disease-related cell functionalities triggered by them. Such mechanistic models use gene expression data to produce an estimation of profiles of signaling or metabolic circuit activity within pathways [20, 24] and have been used to describe the molecular mechanisms behind different biological scenarios such as the explanation on how stress-induced activation of brown adipose tissue prevents obesity [25], the common molecular mechanisms of three cancer-prone genodermatoses [47] or the molecular mechanisms of death and the post-mortem the ischemia of a tissue [26]. Moreover, recent benchmarking of mechanistic modeling methods shows how Hipathia clearly outperform to other competing method [48].…”

“…Thus, mechanistic models of human cell signaling [19] or cell metabolism [20] can provide the functional link between the gene-level data available (gene expression) and the cell phenotype level, allowing the selection of specific disease-related cellular mechanisms of interest. In fact, mechanistic models have helped to understand the disease mechanisms behind different cancers [21–24], the mechanisms of action of drugs [19], and other biologically interesting scenarios such as the molecular mechanisms that explain how stress-induced activation of brown adipose tissue prevents obesity [25] or the molecular mechanisms of death and the post-mortem ischemia of a tissue [26].…”

Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models

“…To achieve so, we have used signaling circuit activities inferred by mechanistic models, as proxies of disease-related cell functionalities triggered by them. Such mechanistic models use gene expression data to produce an estimation of profiles of signaling or metabolic circuit activity within pathways [20,24] and have been used to describe the molecular mechanisms behind different biological scenarios such as the explanation on how stressinduced activation of brown adipose tissue prevents obesity [25], the common molecular mechanisms of three cancer-prone genodermatoses [47] or the molecular mechanisms of death and the postmortem the ischemia of a tissue [26]. Moreover, recent benchmarking of mechanistic modeling methods shows how Hipathia clearly outperform to other competing method [48].…”