A Cellular Automata Model of Oncolytic Virotherapy in Pancreatic Cancer

Exploring neuroblastoma's cellular microenvironment: A novel approach using cellular automata to model Celyvir treatment

Neuroblastoma is a significant health concern in children, as it is one of the most common types of cancer among this age group and is associated with poor survival rates. Currently, there are no effective therapies that significantly improve outcomes for these patients. This study explores the efficacy of Celyvir - an advanced therapy comprising mesenchymal stem cells (MSCs) carrying the oncolytic virus ICOVIR 5 - against neuroblastoma, by means of an individual-based model. A probabilistic cellular automaton was developed to implement the dynamic interactions between neuroblastoma cells, T lymphocytes, and the therapeutic agent Celyvir. The model examines various sizes, shapes, and positions of the tumour within a lattice, along with different infection probabilities associated with the action of Celyvir and various treatment schedules. This analysis identifies the most influential infection probabilities according to the cellular automaton model, and demonstrates that different treatment regimens can effectively eradicate the tumour, in contrast to standard clinical approaches. Additionally, Kaplan-Meier curves have been generated to assess different treatment schedules under specific tumour scenarios, highlighting the importance of precise treatment scheduling to optimise therapeutic outcomes. This study provides insights into the potential of Celyvir in neuroblastoma treatment, emphasising the need to understand tumour dynamics and strategically implement treatment schemes to improve clinical outcomes.

Simulation of Somatic Evolution Through the Introduction of Random Mutation to the Rules of Conway's Game of Life

Introduction

Conway's Game of Life (GOL), and related cellular automata (CA) models, have served as interesting simulations of complex behaviors resulting from simple rules of interactions between neighboring cells, that sometime resemble the growth and reproduction of living things. Thus, CA has been applied towards understanding the interaction and reproduction of single-cell organisms, and the growth of larger, disorganized tissues such as tumors. Surprisingly, however, there have been few attempts to adapt simple CA models to recreate the evolution of either new species, or subclones within a multicellular, tumor-like tissue.

Methods

In this article, I present a modified form of the classic Conway's GOL simulation, in which the three integer thresholds that define GOL (number of neighboring cells, below which a cell will "die of loneliness"; number of neighboring cells, above which a cell will die of overcrowding; and number of neighboring cells that will result in spontaneous birth of a new cell within an empty lattice location) are occasionally altered with a randomized mutation of fractional magnitude during new "cell birth" events. Newly born cells "inherit" the current mutation state of a neighboring parent cell, and over the course of 10,000 generations these mutations tend to accumulate until they impact the behaviors of individual cells, causing them to transition from the sparse, small patterns of live cells characteristic of GOL into a more dense, unregulated growth resembling a connected tumor tissue.

Results

The mutation rate and mutation magnitude were systematically varied in repeated randomized simulation runs, and it was determined that the most important mutated rule for the transition to unregulated, tumor-like growth was the overcrowding threshold, with the spontaneous birth and loneliness thresholds being of secondary importance. Spatial maps of the different "subclones" of cells that spontaneously develop during a typical simulation trial reveal that cells with greater fitness will overgrow the lattice and proliferate while the less fit, "wildtype" GOL cells die out and are replaced with mutant cells.

Conclusions

This simple modeling approach can be easily modified to add complexity and more realistic biological details, and may yield new understanding of cancer and somatic evolution.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12195-024-00828-9.