Dynamics of DNA damage induced pathways to cancer

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.

Figure 1. Flow chart of PKT206 logical model construction and analysis.

Java interface programs were created to extract p53 interactions from the STRING database. We then manually curated the data and used Gene Ontology annotations to connect the network to DNA damage input and apoptosis output. CellNetAnalyzer was used for analysis and simulations, and the results were validated using literature surveys and experimental approaches including western blotting and microarray analysis.

Figure 2. The PKT206 model.

The PKT206 model represented by Cytoscape includes 203 gene/protein nodes, an input node (DNA damage), two output nodes (apoptosis and cellular senescence) and 738 edges. Activation and inhibition connections are represented by blue and red arrows, respectively. The input node was marked by green; the nodes upstream of p53 were marked by yellow; p53 and MDM2 were marked by red, the nodes downstream of p53 were marked by light blue and the output nodes were marked by orange.

Figure 3. Connectivity degree distribution of 206 nodes.

The degree distribution of 206 nodes in the model was obtained by the NetworkAnalyzer plugin for Cytoscape; both axes in the figure are in logarithmic scale.

Figure 4. Validation of the PKT206 model.

(A) Distribution of changes in the dependency matrix of the p53 in silico knock-out compared to the wild-type. The gray cycle represents no effect elements, the orange circle represents ambivalent factors, the light green circle represents weak activators, the pink circle represent weak inhibitors, the dark red circle represents strong inhibitors, and the dark green circle represents strong activators; the direction of the arrow represents the direction of changes in the knock-out. (B) Chk1 (CHEK1) activation is increased in p53 negative background. U2OS cells that have functional p53 and SAOS2 cells that lack functional p53 were treated with 10 µM etoposide for 16 hours. Cell extracts were analyzed by SDS PAGE and western blot analysis using antibodies against total Chk1, ATR and ATM. ATM and ATR phosphorylated Chk1 at Ser 345.

Figure 5. Positive and negative pathways from ATM/ATR to CHEK1.

(A) Positive and negative pathways from ATM/ATR to CHEK1 in p53 wild type cells as known from literature survey; (B) Positive and negative pathways from ATM/ATR to CHEK1 in p53 minus cells. ARF is cyclin-dependent kinase inhibitor 2A. PPM1D is protein phosphatase 1D. pRB is retinoblastoma 1.

Figure 6. Logical steady state analysis of in silico p53 knock-out test.

The nodes with state “1” were represented in green, the nodes with state ”NaN” (un determined) were represented in orange, and the nodes with state “0” were represented in red. (A) P53 wild type when DNA damage was ”ON”; (B) P53 wild type when DNA damage was ”OFF”; (C) P53 mutant when DNA damage was ”ON”; (D) P53 mutant when DNA damage was ”OFF”.