Speaker
Description
Neuroblastomas are solid tumors and represent the most common extracranial tumors in children. The analysis of tumoroid data (artificial organoids capable of reproducing neuroblastoma growth) has revealed a distinctive spatial organization: cancer stem cells tend to cluster at the center of the tumor. A multiscale agent-based neuroblastoma tumoroid model was developed to simulate neuroblastoma growth, and the data provided by this model represent a unique opportunity to investigate the genetic causes of the spatial structures of neuroblastoma tumors. Our goal is to develop a mathematical model of neuroblastoma growth based on these data to better understand its spatial distribution driven by stochastic gene expression and non-local gene interactions, and ultimately to propose more targeted treatments.
We combined a deterministic model of tumor growth with a Piecewise Deterministic Markov Process, which accounts for stochastic gene expression. We conducted a mathematical analysis of this model to prove its well-posedness using semigroup theory and stochastic process theory, and we performed numerical simulations to observe whether the expected spatial distribution emerges.
The model reproduces a single central stem-cell cluster in 1D. Existence of multiple clusters, as highlighted by experimental and computational results, still represent a mathematical challenge. I will discuss the current state of my work and notably how to better capture this specific spatial organization.
