Gómez Tejeda Zañudo Jorge, Scaltriti Maurizio, Albert Réka
1Department of Physics, The Pennsylvania State University, University Park, PA 16802-6300 USA.
2Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215 USA.
Cancer Converg. 2017;1(1):5. doi: 10.1186/s41236-017-0007-6. Epub 2017 Dec 29.
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.
细胞内信号转导网络的机制模型可以解释这些网络如何整合内部和外部输入以产生适当的细胞反应。这些模型可有效地应用于癌细胞，癌细胞在生存或死亡、增殖或静止方面的异常决策可能与信号转导网络节点或边的状态错误有关。
在此，我们基于ER +、HER2 +和PIK3CA突变型乳腺癌的文献，提出了一个全面的ER +乳腺癌信号转导网络和离散动态模型。该网络模型概括了已知的对PI3K抑制剂的耐药机制，并提出了其他耐药可能性。该模型还揭示了比单独抑制PI3K更有效的已知和新型联合干预措施。
使用基于逻辑的离散动态模型能够识别主要归因于信号网络组织的结果，以及那些也依赖于单个事件动力学的结果。这样的基于网络的模型将在高阶治疗组合的合理设计中发挥越来越重要的作用。
