How breast cancer cells respond to the stress of endocrine therapies determines whether they acquire a resistant phenotype or execute a cell death pathway. A successfully executed survival signal then requires determination of whether or not to replicate. How these cell fate decisions are regulated is unclear but evidence suggests that the signals determining these outcomes are highly integrated. Central to the final cell fate decision is signaling from the unfolded protein response, which can be activated following the sensing of stress within the endoplasmic reticulum. Duration of the response to stress is partly mediated by the duration of inositol requiring enzyme-1 (IRE1; ERN) activation following its release from heat shock protein A5 (HSPA5). The resulting signaling appears to use several B-cell lymphoma-2 (BCL2) family members to both suppress apoptosis and activate autophagy. Changes in metabolism induced by cellular stress are key components of this regulatory system, and further adaptation of the metabolome is affected in response to stress. Here we describe the unfolded protein response, autophagy and apoptosis, and how their regulation is integrated. Central topological features of the signaling network that integrate cell fate regulation and decision execution are discussed.

The DNA replication-division cycle of eukaryotic cells is controlled by a complex network of regulatory proteins, called cyclin-dependent kinases, and their activators and inhibitors. Although comprehensive and accurate deterministic models of the control system are available for yeast cells, reliable stochastic simulations have not been carried out because the full reaction network has yet to be expressed in terms of elementary reaction steps. As a first step in this direction, we present a simplified version of the control system that is suitable for exact stochastic simulation of intrinsic noise caused by molecular fluctuations and extrinsic noise because of unequal division. The model is consistent with many characteristic features of noisy cell cycle progression in yeast populations, including the observation that mRNAs are present in very low abundance (Ϸ1 mRNA molecule per cell for each expressed gene). For the control system to operate reliably at such low mRNA levels, some specific mRNAs in our model must have very short half-lives (<1 min). If these mRNA molecules are longer-lived (perhaps 2 min), then the intrinsic noise in our simulations is too large, and there must be some additional noise suppression mechanisms at work in cells.cyclin-dependent kinase ͉ gene expression ͉ network dynamics ͉ stochastic model ͉ mRNA turnover

Cancers of the breast and other tissues arise from aberrant decision-making by cells regarding their survival or death, proliferation or quiescence, damage repair or bypass. These decisions are made by molecular signaling networks that process information from outside and from within the breast cancer cell and initiate responses that determine its survival and reproduction. Because the molecular logic of these circuits is difficult to comprehend by intuitive reasoning alone, we present some preliminary mathematical models of the basic decision circuits in breast cancer cells, with an eye to understanding better their susceptibility or resistance to endocrine therapy.

Multisite phosphorylation of CDK target proteins provides the requisite nonlinearity for cell cycle modeling using elementary reaction mechanisms.Stochastic simulations, based on Gillespie's algorithm and using realistic numbers of protein and mRNA molecules, compare favorably with single-cell measurements in budding yeast.The role of transcription–translation coupling is critical in the robust operation of protein regulatory networks in yeast cells.

Approximately 70% of all newly diagnosed breast cancers express estrogen receptor (ER)-α. Although inhibiting ER action using targeted therapies such as fulvestrant (ICI) is often effective, later emergence of antiestrogen resistance limits clinical use. We used antiestrogen-sensitive and -resistant cells to determine the effect of antiestrogens/ERα on regulating autophagy and unfolded protein response (UPR) signaling. Knockdown of ERα significantly increased the sensitivity of LCC1 cells (sensitive) and also resensitized LCC9 cells (resistant) to antiestrogen drugs. Interestingly, ERα knockdown, but not ICI, reduced nuclear factor (erythroid-derived 2)-like (NRF)-2 (UPR-induced antioxidant protein) and increased cytosolic kelch-like ECH-associated protein (KEAP)-1 (NRF2 inhibitor), consistent with the observed increase in ROS production. Furthermore, autophagy induction by antiestrogens was prosurvival but did not prevent ERα knockdown-mediated death. We built a novel mathematical model to elucidate the interactions among UPR, autophagy, ER signaling, and ROS regulation of breast cancer cell survival. The experimentally validated mathematical model explains the counterintuitive result that knocking down the main target of ICI (ERα) increased the effectiveness of ICI. Specifically, the model indicated that ERα is no longer present in excess and that the effect on proliferation from further reductions in its level by ICI cannot be compensated for by increased autophagy. The stimulation of signaling that can confer resistance suggests that combining autophagy or UPR inhibitors with antiestrogens would reduce the development of resistance in some breast cancers.