Kinetic modeling-based detection of genetic signatures that provide chemoresistance via the E2F1-p73/DNp73-miR-205 network

Drug resistance is a major cause of deaths from cancer. E2F1 is a transcription factor involved in cell proliferation, 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. Using a regulatory map developed to summarize knowledge on E2F1 and 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 anticancer drugs. By perturbing the model parameters, we simulated 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 such as TGFb-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.