The Expanded p53 Interactome as a Predictive Model for Cancer Therapy

The tumor suppressor gene TP53 is implicated in the majority of all human cancers, thus pivotal to genomic integrity. There are more than 72,000 scientific publications describing p53’s function, yet, due to the complexity of its interactions we are still far from fully elucidating p53’s role in tumorigenesis. Computational methodologies are novel tools to depict and dissect complex disease networks. The Boolean PKT206 p53 – DNA damage model has previously demonstrated good predictive capability for p53 wild-type and null tumors in various in silico knockouts. Here, we have expanded PKT206 to generate a more clinically robust representation of p53 dynamics. The new PMH260 model incorporates 260 nodes representing genes, with 980 interactions between them representing inhibitions and activations. Additional biological outputs, including angiogenesis, cell cycle arrest and DNA repair were also amalgamated into the model. Three in silico knockouts of highly connected nodes (p53, MDM2 and FGF2) were generated and logical steady state analysis and dependency relationships applied. 71 % of predictions were considered true from superimposition of human osteosarcoma and HCT116 microarray profiles. In silico knockout analysis revealed 98 potential novel predictions, of which 13 were validated by literature; 83 % of them were overlapping with PKT206. Thus the expanded Boolean PMH260 model offers a promising platform for clinical potential in targeted cancer therapeutics.