The expanded p53 interactome as a predictive model for cancer therapy

Hussain, M; Tian, K; Mutti, L; Krstic-Demonacos, M; Schwartz, JM

Abstract

The tumour suppressor gene TP53 is implicated in
the majority of all human cancers, thus pivotal to
genomic integrity. Even though over 72,000 PubMed
publications are linked with the keyword p53 and this
number is continuously increasing, 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 wildtype
and null tumours 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 determined. 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.

Citation

Hussain, M., Tian, K., Mutti, L., Krstic-Demonacos, M., & Schwartz, J. (2015). The expanded p53 interactome as a predictive model for cancer therapy. Genomics and computational biology, 1(1), e20. https://doi.org/10.18547/gcb.2015.vol1.iss1.e20

• Journal Article Type: Article
• Acceptance Date: Aug 20, 2015
• Publication Date: Sep 18, 2015
• Deposit Date: Mar 22, 2016
• Publicly Available Date: Apr 5, 2016
• Journal: Genomics and Computational Biology
• Volume: 1
• Issue: 1
• Pages: e20
• DOI: https://doi.org/10.18547/gcb.2015.vol1.iss1.e20
• Publisher URL: http://dx.doi.org/10.18547/gcb.2015.vol1.iss1.e20
• Related Public URLs: https://genomicscomputbiol.org/ojs/index.php/GCB/index