Cancer Res. 2013 Jun 15;73(12):3511-24.

Kinetic Modeling-Based Detection of Genetic Signatures that Provide Chemoresistance via the E2F1-p73/DNp73-miR-205 Network.

Vera J, Schmitz U, Lai X, Engelmann D, Khan FM, Wolkenhauer O, Pützer BM.
Drug resistance is a major cause of deaths from cancer. E2F1 is a transcription factor involved in cell cycle progression, apoptosis and metastasis through an intricate regulatory network, which includes other transcription factors like p73 and cancer-related microRNAs like miR-205. To investigate the emergence of drug resistance, we developed a methodology that integrates experimental data with a network biology and kinetic modeling approach. Using a regulatory map developed to summarize knowledge on E2F1 its interplay with p73/DNp73 and miR-205 in cancer drug responses, we derived a kinetic model that represents the network response to certain genotoxic and cytostatic anti-cancer drugs. By perturbing the model parameters we mimic heterogeneous cell configurations referred to as in silico cell lines. These were used to detect genetic signatures characteristic for single or double drug resistance. We identified a signature composed of high E2F1 and low miR-205 expression that promotes resistance to genotoxic drugs. In this signature, downregulation of miR-205 can be mediated by an imbalance in the p73/DNp73 ratio or by dysregulation of other cancer-related regulators of miR-205 expression like TGFβ-1 or TWIST1. In addition, we found that a genetic signature composed of high E2F1, low miR-205 and high ERBB3 can render tumor cells insensitive to both cytostatic and genotoxic drugs. Our model simulations also suggested that conventional genotoxic drug treatment favors selection of chemoresistant cells in genetically heterogeneous tumors, in a manner requiring dysregulation of incoherent feedforward loops that involve E2F1, p73/DNp73 and miR-205.
Vorheriger Beitrag
The E2F1-miRNA Cancer Progression Network.
Nächster Beitrag
Hybrid modeling of the crosstalk between signaling and transcriptional networks using ordinary differential equations and multi-valued logic.