In vitro and in silico multidimensional modeling of oncolytic tumor virotherapy dynamics

Abstract

Tumor therapy with replication competent viruses is an exciting approach to cancer eradication where viruses are engineered to specifically infect, replicate, spread and kill tumor cells. The outcome of tumor virotherapy is complex due to the variable interactions between the cancer cell and virus populations as well as the immune response. Oncolytic viruses are highly efficient in killing tumor cells in vitro, especially in a 2D monolayer of tumor cells, their efficiency is significantly lower in a 3D environment, both in vitro and in vivo. This indicates that the spatial dimension may have a major influence on the dynamics of virus spread. We study the dynamic behavior of a spatially explicit computational model of tumor and virus interactions using a combination of in vitro 2D and 3D experimental studies to inform the models. We determine the number of nearest neighbor tumor cells in 2D (median = 6) and 3D tumor spheroids (median = 16) and how this influences virus spread and the outcome of therapy. The parameter range leading to tumor eradication is small and even harder to achieve in 3D. The lower efficiency in 3D exists despite the presence of many more adjacent cells in the 3D environment that results in a shorter time to reach equilibrium. The mean field mathematical models generally used to describe tumor virotherapy appear to provide an overoptimistic view of the outcomes of therapy. Three dimensional space provides a significant barrier to efficient and complete virus spread within tumors and needs to be explicitly taken into account for virus optimization to achieve the desired outcome of therapy.

Modeling the Efficacy of Oncolytic Adenoviruses In Vitro and In Vivo: Current and Future Perspectives

“…The majority of studies show that OAds lyse cells much less effectively in 3D compared to 2D cultures, suggesting that spatial organization regulates virus spread. A recent study comparing experimental 2D vs. 3D modeling to computational modeling to determine the efficacy of OAd spread confirmed this hypothesis [66]. Researchers found the average number of "neighbor" cells was higher in 3D models (median = 16) than 2D (median = 6), but virus spread was slower and less efficient, indicating that the additional dimension must be considered to accurately predict clinical efficacy.…”

Multiscale moving boundary modelling of cancer interactions with a fusogenic oncolytic virus: The impact of syncytia dynamics

“…This tumour reduction can be explained by the fact that syncytia diffusion (which depends on OV and ECM spatial distribution) leads to the accumulation of syncytia structures in areas with uninfected and infected tumour cells (the infected tumour cells releasing more OV), which ultimately causes more tumour destruction. We also note that there is no significant difference between the model dynamics with f s described by either equations (18) or (19). For a visual description of the effect of density-dependent syncytia diffusion on the spatial distribution of total tumour (unin-fected+infected+syncytia cells) at different micro-macro simulation stages please see Figure 17.…”

“…The majority of the mathematical models that focus on fusogenic oncolytic viruses consider only the implicit (temporal) dynamics of syncytia cancer cells [13,14,15]. There are also a few mathematical models that consider the explicit dynamics of the syncytia; see for example [16] for a temporal (ODE) model, and [17,18] for spatio-temporal models. Moreover, the large majority of these models focus on a single-scale dynamics of viruses spread among cancer cells.…”

Non-local multiscale approaches for tumour-oncolytic viruses interactions

“…There are currently many mathematical studies that focus on cancer growth and its interactions with the oncolytic viruses; see, for example [14,18,19,23,26,35,32,52,55] and the references therein. While most of these models focus on the temporal dynamics of OVs-tumour interactions (mainly because of the availability of temporal data for tumour growth in the presence/absence of various OV therapies), recent advances in tumour imaging have led to the recent development of models investigating the spatio-temporal dynamics of tumour-OV interactions [7,29,33,46,56]. The large majority of these temporal and spatio-temporal models focus tumour-OV interactions at single spatial and/or temporal scale.…”