Stochastic Boolean Model of Death Signaling in MCF7:5C Predicts Cell Death Inducers and Inhibitors

Disrupted cell death signaling contributes significantly to the inappropriate survival of cancer cells, enabling them to evade the apoptotic processes that normally eliminate damaged or abnormal cells. To assess the impact of disrupted cell death signaling on cancer cell survival, we developed a continuous-time stochastic Boolean model of apoptosis signaling in MCF7:5C human breast cancer cells that are resistant to estrogen deprivation and undergo apoptosis in response to estrogen. The model was calibrated to replicate the dynamics of key apoptosis regulators in MCF7:5C cells before and after 17{beta}-estradiol (E2) treatment. Subsequently, the calibrated model was used to predict both the single and double perturbations that inhibit cell death in estradiol-treated MCF7:5C cells, and additional single interventions capable of reinducing apoptosis. For example, strains with single-gene deletion of PERK, eIF2, CHOP, or ATF4 enabled E2-treated MCF7:5C cells to evade apoptosis. However, additional inhibition of SRC, PI3K, ESR1, CEBPB, or MAPK8 restored apoptotic responses in these resistant cells. We identified 30 resistant cell strains with double mutations and 506 drug-target combinations capable of inducing four distinct modes of cell death: direct CASP7 activation, extrinsic apoptosis, intrinsic apoptosis, and combined intrinsic/extrinsic apoptosis. Overall, our model successfully explains the drug response behavior of MCF7:5C cells, predicts mechanisms of drug resistance, and suggests treatment strategies to overcome this resistance.