Abstract CN05-03: Personalized cancer diagnostics and therapeutics based on the computational modeling of signal transduction networks

Abstract

Abstract Signalling pathways exert finely tuned control over cell fate decisions that ultimately determine the behaviour of cancer cells. It could therefore be expected that the dynamics of key pathway activation may contain prognostically relevant information over and above that which is contained in the static nature of traditional biomarkers. To investigate this hypothesis we have characterised the network architecture regulating JNK stress signalling in neuroblastoma cells and applied an experimentally calibrated and validated computational model of this network to extract prognostic information from neuroblastoma patient-specific simulations of JNK activation. Survival analysis based upon the dynamics of these simulations revealed that an inability to initiate switch-like JNK signalling in silico was significantly associated with poor overall survival for both MYCN amplified and non-amplified neuroblastoma patients. Furthermore, our analysis demonstrated that in order to extract prognostic information from a signalling pathway, deciphering the extant network structure is a vital consideration in model development. We also show that network based analysis can lead to the discovery of new therapeutic targets. Citation Format: Walter Kolch, Dirk Fey, Melinda Halasz, Nora Rauch, Amaya Garcia Munoz, Ruth Pilkington, Boris N. Kholodenko, David R. Croucher, Sean P. Kennedy, Jordan F. Hastings, Frank Westermann, Daniel Dreidax, Matthias Fischer, David Duffy, Aleksandar Krstic, Thomas Schwarzl. Personalized cancer diagnostics and therapeutics based on the computational modeling of signal transduction networks. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr CN05-03.