Drug resistance mechanisms and combinatorial drug treatments in breast cancer: a network modeling approach

Mechanistic models of within-cell signal transduction can explain how these networks integrate internal and external inputs to give rise to the appropriate cellular response. These models can be fruitfully used in cancer cells, whose aberrant decision-making regarding their survival, death, or proliferation can be connected to errors in the state of nodes or edges of the signal transduction network. Here we present a comprehensive network, and discrete dynamic model, of signal transduction in breast cancer based on the literature of ER+, HER2+, and PIK3CA-mutant breast cancers. The network model recapitulates known resistance mechanisms to PI3K inhibitors, suggests other possibilities for resistance, and reveals known and novel combinatorial interventions that are more effective than PI3K inhibition alone. The use of a discrete dynamics enables the identification of results that are due to the organization of the signaling network, and those that also depend on the kinetics of individual events. Network-based models such as this will play an increasing role in the rational design of high-order therapeutic combinations.