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October 3, 2016
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October 11, 2016
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Webpage:http://www.ucd.ie/sbi/team/principalinvestigators/boriskholodenko/

Research Field
  • genetics, genomics and molecular biology
  • cancer/oncology
  • Other - please suggest keyword(s):
My WorkMy lab develops systems biology approaches to solve fundamental problems in biology and biomedicine (Kholodenko, B.N. (2006) Cell-signalling dynamics in time and space, Nat Rev Mol Cell Biol. 7,165-176; Nakakuki et al (2010) Ligand-specific c-Fos expression emerges from the spatiotemporal control of ErbB network dynamics, Cell 141, 884-896). In my lab, computational modelling and experimental research is fully integrated for designing new diagnostic and personalised therapeutic approaches to cancer, based on a systems-level, mechanistic understanding of cellular signal transduction networks. We demonstrated that personalised models of network dynamics can be used to uncover fundamental, hidden mechanisms of variable treatment responses in individual patients. We have recently shown that kinase inhibitor resistance emerges from kinase dimerization, which is an obligatory step in the physiological and oncogenic activation of numerous kinases (Kholodenko B.N. Cell reports, 2015, 12:1939). Our work predicted that two inhibitors against the same kinase, while ineffective on their own, can abolish resistance when combined. Understanding network’s dynamic regulation by experiments and modelling paves the way to new prognostic and predictive biomarkers and personalized treatments (Fey et al (2015) Signaling pathway models as biomarkers: Patient-specific simulations of JNK activity predict the survival of neuroblastoma patients. Science signaling 8, ra130, doi:10.1126/scisignal.aab0990).
Outline of Potential Research projectsAdvancing personalised cancer medicine using computational models of tumour signalling.

Cancer is a dynamic disease of dysregulated cellular networks. The malfunctioning of these networks is brought about by genetic aberrations, including mutations, gene amplifications, fusions, silencing and alternatively spliced genes. Most common mutations in cancer affect signal transduction networks – the dynamic control and communication systems in a cell. The dynamic states of these networks in cancer patients vary over time, for example in response to drugs, and are intimately linked to individual pathogenetic changes. My lab has developed personalised models of the key signalling pathways (PI3K/AKT, RASSF/MST/LATS, RAS/RAF/MEK/ERK and DNA Damage Repair/p53/MDM2/JNK) that functionally describe the complex pathophysiological changes underlying disease manifestations in individual patients. These models connect the static genetic aberrations with dynamic pathological changes in the network responses to environmental and drug perturbations. The main Specific Aims of this project are twofold: i) to map tumour data of responding and non-responding patients onto personalised cancer network models, and ii) to test the clinical utility of these personalised models as predictive or prognostic markers. The proposed project will use biochemical experiments in cell lines and omics and clinical data to explore mechanisms of drug resistance. Working with computational modellers, the candidate will test ways to overcome resistance to targeted and chemotherapeutic drugs, which almost inevitably occurs in cancer patients, using predictions of dynamic network models. The project will use patient’s omics data and measure key protein abundances/activities in tumour samples. This will allow for connecting patient’s molecular profiles with their tumour responses to targeted and chemotherapy drugs. A particular innovation is that patient’s omics data are not used directly to predict the clinical outcome, but are fed into personalised computer simulations that take the underlying disease mechanisms into account. The candidate will explore the use of personalised dynamic models as prognostic and predictive biomarkers for individual patients.