How can we kill cancer cells: insights from the computational models of apoptosis

TL;DR

This paper uses computational Monte Carlo models to explore how variability in apoptotic signaling contributes to cancer cell resistance and fractional killing, providing new insights into apoptosis mechanisms.

Contribution

It introduces in silico Monte Carlo simulations to study apoptotic pathway variability and its role in cancer cell survival and resistance.

Findings

Cell-to-cell variability influences apoptosis kinetics.

Stochastic signaling variability can cause fractional cell death.

Computational models reveal mechanisms of chemoresistance.

Abstract

Cancer cells are widely known to be protected from apoptosis, which is a major hurdle to successful anti-cancer therapy. Over-expression of several anti-apoptotic proteins, or mutations in pro-apoptotic factors, has been recognized to confer such resistance. Development of new experimental strategies, such as in silico modeling of biological pathways, can increase our understanding of how abnormal regulation of apoptotic pathway in cancer cells can lead to tumour chemoresistance. Monte Carlo simulations are in particular well suited to study inherent variability, such as spatial heterogeneity and cell-to-cell variations in signaling reactions. Using this approach, often in combination with experimental validation of the computational model, we observed that large cell-to-cell variability could explain the kinetics of apoptosis, which depends on the type of pathway and the strength of stress stimuli. Most importantly, Monte Carlo simulations of apoptotic signaling provides unexpected insights into the mechanisms of fractional cell killing induced by apoptosis-inducing agents, showing that not only variation in protein levels, but also inherent stochastic variability in signaling reactions, can lead to survival of a fraction of treated cancer cells.