A network modeling approach to elucidate drug resistance mechanisms and predict combinatorial drug treatments in breast cancer

Jorge Gómez Tejeda Zañudo,Maurizio Scaltriti,Réka Albert

doi:10.1186/s41236-017-0007-6

Abstract

BackgroundMechanistic models of within-cell signal transduction networks 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 or death, proliferation or quiescence can be connected to errors in the state of nodes or edges of the signal transduction network.ResultsHere we present a comprehensive network, and discrete dynamic model, of signal transduction in ER+ breast cancer based on the literature of ER+, HER2+, and PIK3CA-mutant breast cancers. The network model recapitulates known resistance mechanisms to PI3K inhibitors and suggests other possibilities for resistance. The model also reveals known and novel combinatorial interventions that are more effective than PI3K inhibition alone.ConclusionsThe use of a logic-based, discrete dynamic model enables the identification of results that are mainly 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.

Highlights

Mechanistic models of within-cell signal transduction networks can explain how these networks integrate internal and external inputs to give rise to the appropriate cellular response
A network model of oncogenic signal transduction in estrogen receptor (ER)+ breast cancer We constructed a comprehensive discrete dynamic network model of signal transduction in ER+ breast cancer based on the literature of ER+, Human epidermal growth factor receptor 2 (HER2)+, and PIK3CA-mutant breast cancers (Fig. 3)
The model incorporates the findings of resistance studies in the context of Phosphatidylinositol – 4 (PI3K) inhibitors, mTORC inhibitors, Protein kinase B (AKT) inhibitors, MAPK inhibitors, Receptor tyrosine kinase (RTK) inhibitors, CDK4/6 inhibitors, and ER inhibitors/degraders, the feedback mechanisms and adaptive cellular responses identified during these studies, and includes the recent

Summary

Introduction

Mechanistic models of within-cell signal transduction networks can explain how these networks integrate internal and external inputs to give rise to the appropriate cellular response. The methods to reach these goals will have to take into account the genomic and phenotypic diversity of tumors, the variety of resistance mechanisms, and the intrinsically combinatorial nature of the problem (Higgins & Baselga, 2011; Friedman et al, 2015; Johannessen & Boehm, 2017; Meric-Bernstam & Mills, 2012). This makes the currently used strategies ineffective and calls for new approaches that fall under the broad umbrella of the systems biology paradigm (Werner et al, 2014; Archer et al, 2016)

Results

Discussion

Conclusion