Dynamics of DNA damage induced pathways to cancer

• Published in: PloS one Vol. 8; no. 9; p. e72303
• Main Authors: Tian, Kun, Rajendran, Ramkumar, Doddananjaiah, Manjula, Krstic-Demonacos, Marija, Schwartz, Jean-Marc
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 04.09.2013; Public Library of Science (PLoS)
• Subjects: Analysis; Anemia; Apoptosis; Biocompatibility; Biomedical materials; Breast cancer; Cancer; Cancer genetics; Cell cycle; Chemoresistance; Chemotherapy; CHK1 protein; Colon; Colon cancer; Computer simulation; Deoxyribonucleic acid; Development and progression; DNA; DNA damage; DNA Damage - genetics; DNA Damage - physiology; DNA microarrays; DNA repair; DNA-Binding Proteins - genetics; Fibroblast growth factor 2; Genes; Genomes; Growth factors; HCT116 Cells; Health aspects; Humans; Information processing; Ionizing radiation; Kinases; Life sciences; Literature reviews; Mathematical models; Mimicry; Mutation; Neoplasms - etiology; Neoplasms - genetics; Osteosarcoma; p53 Protein; Platelet-derived growth factor; Prediction models; Proteins; Receptors; Senescence; Signal Transduction - genetics; Signal Transduction - physiology; Steady state models; Therapeutic applications; Tumor proteins; Tumor Suppressor Proteins - genetics; Tumors; United Kingdom > UK
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0072303

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.