Abstract
Chemotherapy is commonly used in cancer treatments, however only 25% of cancers are responsive and a significant proportion develops resistance. The p53 tumour suppressor is crucial for cancer development and therapy, but has been less amenable to therapeutic applications due to the complexity of its action, reflected in 66,000 papers describing its function. Here we provide a systematic approach to integrate this information by constructing a large-scale logical model of the p53 interactome using extensive database and literature integration. The model contains 206 nodes representing genes or proteins, DNA damage input, apoptosis and cellular senescence outputs, connected by 738 logical interactions. Predictions from in silico knock-outs and steady state model analysis were validated using literature searches and in vitro based experiments. We identify an upregulation of Chk1, ATM and ATR pathways in p53 negative cells and 61 other predictions obtained by knockout tests mimicking mutations. The comparison of model simulations with microarray data demonstrated a significant rate of successful predictions ranging between 52% and 71% depending on the cancer type. Growth factors and receptors FGF2, IGF1R, PDGFRB and TGFA were identified as factors contributing selectively to the control of U2OS osteosarcoma and HCT116 colon cancer cell growth. In summary, we provide the proof of principle that this versatile and predictive model has vast potential for use in cancer treatment by identifying pathways in individual patients that contribute to tumour growth, defining a sub population of “high” responders and identification of shifts in pathways leading to chemotherapy resistance.
Highlights
The p53 protein has been one of the most studied proteins since its discovery in 1979. It plays a central role in the regulation of cell survival and cancer development; p53 mutations are found in more than 50% of human tumours and alterations or lack of p53 function has been linked to most types of cancer cells
We present a logical model of the p53 system that integrates 203 genes/proteins, DNA damage input, apoptosis and cellular senescence outputs, connected by 738 logical interactions compiled from existing databases and the scientific literature
Nodes represent genes or associated proteins that interact with p53, and edges represent the interactions between them
Summary
The p53 protein has been one of the most studied proteins since its discovery in 1979. The p53 protein acts as a transcription factor, which regulates the expression of a large number of downstream genes by complex mechanisms [1] It has anti-proliferative effects such as cell cycle arrest, apoptosis, and cell senescence in response to various stress signals. The detailed kinetics of only a subset of these interactions is known [11] For this reason, we hypothesized that our understanding of p53 function can be enhanced by the systematic integration of such qualitative knowledge into a large-scale, consistent logical model. Schlatter’s group constructed a Boolean network based on literature searches and described the behaviour of both intrinsic and extrinsic apoptosis pathways in response to diverse stimuli Their model revealed the importance of crosstalk and feedback loops in controlling apoptotic pathways [12]. It is found that the PKT206 model is a promising predictive tool that can increase our understanding of the complex mechanisms of p53 pathways and provides a novel approach to personalized cancer therapy
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
