Gene Regulatory Network Inference reveals tcf4 as a key a player in neuroblastoma gene expression circuitry
- 1Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell / Inria
Abstract
Neuroblastoma (NB), a pediatric cancer arising from disrupted sympathetic neuron differentiation, exhibits marked heterogeneity and limited therapeutic options. To better understand its molecular circuitry dynamics, we applied CardamomOT, a novel Gene Regulatory Network (GRN) inference framework, to single-cell RNA-seq data from patient-derived tumoroids. This approach models gene regulation via piecewise deterministic Markov processes, capturing transcriptional bursting and protein-mediated feedback, overcoming limitations of RNA velocity (e.g., gene independence and lack of biological time). We identified a continuous chromaffin-to-sympathoblast differentiation trajectory along which we selected 85 dynamically relevant genes enriched in cell cycle and DNA replication functions. Notably, 9 genes overlapped with those driving normal sympathoadrenal differentiation, underscoring tumor-normal tissue similarity. The inferred 85-genes network reproduced quite well experimental gene expression patterns in silico, and allowed to predict protein-level dynamics. Furthermore, it allowed to predict the effect of perturbations (both knock-out and overexpression) of hub genes (e.g., tcf4 and PLK1). We show that those perturbations significantly altered cell fate proportions in silico, with tcf4 KO increasing chromaffin-like cells and reducing proliferative late sympathoblasts. Predictions regarding tcf4 were tested using drug inhibition as a proxy for the gene KO. Using the BET inhibitor JQ1 indeed induced profound effect on the transcriptomic identity of our tumoroids. All of the 50 predicted tcf4 target genes were found to be significantly altered by JQ1 treatment. Finally cell fate proportions were also altered ex vivo closely resembling the predicted output. Our work therefore demonstrates that NB tumoroids retain a dynamic, differentiation-like architecture amenable to GRN modeling. Predicted druggable targets offer testable therapeutic avenues, including repurposing BET inhibitors or PLK1 inhibitors, potentially in combination.
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BibTeX
@misc{koering2026gene,
title = {{Gene Regulatory Network Inference reveals tcf4 as a key a player in neuroblastoma gene expression circuitry}},
author = {C. Koering and E. Vallin and F. Picard and S. Gonin-Giraud and O. Gandrillon},
howpublished = {bioRxiv},
year = {2026},
month = jul,
doi = {10.64898/2026.07.07.737136},
url = {https://www.biorxiv.org/content/10.64898/2026.07.07.737136v2},
note = {CC BY},
}
RIS
TY - UNPB
TI - Gene Regulatory Network Inference reveals tcf4 as a key a player in neuroblastoma gene expression circuitry
AU - C. Koering
AU - E. Vallin
AU - F. Picard
AU - S. Gonin-Giraud
AU - O. Gandrillon
PY - 2026
DA - 2026/07/07
PB - bioRxiv
DO - 10.64898/2026.07.07.737136
UR - https://www.biorxiv.org/content/10.64898/2026.07.07.737136v2
AB - Neuroblastoma (NB), a pediatric cancer arising from disrupted sympathetic neuron differentiation, exhibits marked heterogeneity and limited therapeutic options. To better understand its molecular circuitry dynamics, we applied CardamomOT, a novel Gene Regulatory Network (GRN) inference framework, to single-cell RNA-seq data from patient-derived tumoroids. This approach models gene regulation via piecewise deterministic Markov processes, capturing transcriptional bursting and protein-mediated feedback, overcoming limitations of RNA velocity (e.g., gene independence and lack of biological time). We identified a continuous chromaffin-to-sympathoblast differentiation trajectory along which we selected 85 dynamically relevant genes enriched in cell cycle and DNA replication functions. Notably, 9 genes overlapped with those driving normal sympathoadrenal differentiation, underscoring tumor-normal tissue similarity. The inferred 85-genes network reproduced quite well experimental gene expression patterns in silico, and allowed to predict protein-level dynamics. Furthermore, it allowed to predict the effect of perturbations (both knock-out and overexpression) of hub genes (e.g., tcf4 and PLK1). We show that those perturbations significantly altered cell fate proportions in silico, with tcf4 KO increasing chromaffin-like cells and reducing proliferative late sympathoblasts. Predictions regarding tcf4 were tested using drug inhibition as a proxy for the gene KO. Using the BET inhibitor JQ1 indeed induced profound effect on the transcriptomic identity of our tumoroids. All of the 50 predicted tcf4 target genes were found to be significantly altered by JQ1 treatment. Finally cell fate proportions were also altered ex vivo closely resembling the predicted output. Our work therefore demonstrates that NB tumoroids retain a dynamic, differentiation-like architecture amenable to GRN modeling. Predicted druggable targets offer testable therapeutic avenues, including repurposing BET inhibitors or PLK1 inhibitors, potentially in combination.
ER -