(620a) Multiscale Cancer Modeling and Insilico Oncology: Emerging Computational Frontiers in Basic and Translational Cancer Research

We describe current advances in multiscale computational modeling and simulation approaches in our laboratory combining models in structural biology and those in systems biology. Our approach combines hybrid and multiscale methods which we have developed with network-based methods in systems biology to enable the construction of quantitative models of signaling networks while retaining the crucial elements of molecular specificity, which are highly relevant to profiling and predictive modeling of clinical mutations in many transformed cell lines. We describe two applications in cell signaling with quantitative and predictive capabilities. (1) To investigate the role of the Anaplastic Lymphoma Kinase (ALK) receptor in pediatric neuroblastoma, we present a computational modeling and simulation approach to delineate molecular-level mechanisms of activation of protein receptor tyrosine kinases and describe clinical implications of the mutations. We show here that our results shed molecular-level insight into the various mechanisms governing such transforming mutations at the level of kinase activity and are remarkably consistent with experimental observations. This study on ALK with enables predictions of driver oncogenic mutations with low false-positive rates, and can hence serve an important in silico tool toward personalized cancer therapy. (2) Somatic mutations in, or over-expression of, the receptor tyrosine kinases of the ErbB family are found in many cancers and is correlated with a poor prognosis. Particularly, clinically identified mutations found in non-small-cell lung cancer (NSCLC) of ErbB1/HER1/EGFR have been shown to increase its basal kinase activity and patients carrying these mutations respond remarkably to the small tyrosine kinase inhibitor gefitinib. Here, we analyze the potential effects of the currently catalogued clinically identified mutations in the ErbB family kinase domains on the molecular mechanisms of kinase activation. Recently, we identified conserved networks of hydrophilic and hydrophobic interactions characteristic to the active and inactive conformation, respectively. Here, we show that the clinically identified mutants influence the kinase activity in distinctive fashion by affecting the characteristic interaction networks. We show that by analyzing the properties of these networks one can gain valuable insight into cohort-sensitivity to inhibition as well as into inhibitor-resistance mechanisms.

Related References:

1.    Molecular Systems Biology of ErbB1 Signaling: Bridging the Gap through Multiscale Modeling and High-Performance Computing, Andrew Shih, Jeremy Purvis, R. Radhakrishnan, Molecular Biosystems (A Royal Society of Chemistry Journal), 4: 1151-1159, 2008.  Pubmed ID: 19396377

2.    Molecular Dynamics Analysis of Conserved Hydrophobic and Hydrophilic Bond Interaction Networks in ErbB Family Kinases, A. Shih, S. E. Telesco, S. H. Choi, M. A. Lemmon, R. Radhakrishnan, Biochemical Journal, 2011, 436(2), 241-251. Pubmed ID: 21426301.

3.    Analysis of Somatic Mutations in Cancer: Molecular Mechanisms of Activation in the ErbB family of Receptor Tyrosine Kinases, A. J. Shih, S. E. Telesco, R. Radhakrishnan, Cancers, 2011, 3(1), 1195-1231; doi:10.3390/cancers3011195. Pubmed ID: 21701703.

4.     Erlotinib binds both inactive and active conformations of the EGFR tyrosine kinase domain, J. H. Park¹, Y. Liu¹, M. A. Lemmon*, R. Radhakrishnan*, Biochem J., 2012, 448(3), 417-423; 1. Equal contribution, * co-corresponding authors.

5.    A Multiscale Modeling Approach to Investigate Molecular Mechanisms of Pseudokinase Activation and Drug Resistance in the HER3/ErbB3 Receptor Tyrosine Kinase Signaling Network, S. E. Telesco, A. J. Shih, F. Jia, R. Radhakrishnan, Molecular Biosystems (RSC Journal), 2011, 7 (6), 2066 - 2080. DOI: 10.1039/c0mb00345j. Pubmed ID: 21509365.

6.     Computational Methodology for Mechanistic Profiling of Kinase Domain Mutations in Cancers, P. J. Huwe, and R. Radhakrishnan, Proceedings of the IEEE, 5^th International Advanced Research Workshop on In Silico Oncology and Cancer Investigation, 2013, in press. An open access version of the article is also available at http://www.5th-iarwisoci.iccs.ntua.gr/