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• W2951789849 abstract "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. 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.Here 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.The 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." @default.
• W2951789849 created "2019-06-27" @default.
• W2951789849 creator A5002819777 @default.
• W2951789849 creator A5012905268 @default.
• W2951789849 creator A5042886213 @default.
• W2951789849 date "2017-12-01" @default.
• W2951789849 modified "2023-10-09" @default.
• W2951789849 title "A network modeling approach to elucidate drug resistance mechanisms and predict combinatorial drug treatments in breast cancer" @default.
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• W2951789849 doi "https://doi.org/10.1186/s41236-017-0007-6" @default.
• W2951789849 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5876695" @default.
• W2951789849 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/29623959" @default.
• W2951789849 hasPublicationYear "2017" @default.
• W2951789849 type Work @default.
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