Investigation of stochasticity in TRAIL signaling cancer model

In cancer, apoptosis or programmed cell death has been demonstrated through the tumor necrosis factor related apoptosis-inducing ligand (TRAIL) signal transduction. As a result, TRAIL-based therapies have been widely investigated to fight cancers. However, several malignant cancer types still remain resistant to TRAIL. Recently, we developed a dynamic computational model to investigate the resistance mechanisms in TRAIL-stimulated human fibrosarcoma (HT1080) cells. The macroscopic average-cell response model, based on the law of mass action and signaling flux conservation, successfully simulates the semi-quantitative temporal profiles of cell survival (IκB, JNK, p38) and apoptotic (caspase-8 and -3) molecules in wildtype and several mutants (FADD, RIP1 and TRAF2 knockdowns or KD). However, cancer populations are known to be highly heterogeneous, and various studies have demonstrated the importance of stochasticity and variability for phenotypic diversity between identical cells. Here, we extend our original model to investigate the effect of such fluctuations on TRAIL signaling response by adopting probabilities of signaling reactions through the Gillespie algorithm. Notably, when we stimulated the model 1000 times to indicate the variability of 1000 single cell responses in all 4 experimental conditions with different levels of stochasticity, we notice that TRAF2 KD produced the most variable signaling response. This variance subsequently affected the level of cellular apoptosis analysed through the cell-survival metric (CSM). Our work highlights the necessity to understand variable responses of cell signaling reactions to different levels of stochasticity. Thus, prior to the actual development of potential drug targets for killing cancer cells, the effect of stochastic variance could be investigated through dynamic models.