Computational modelling of cancerous mutations in the EGFR/ERK signalling pathway

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• 作者：Richard J Orton (1) (2)
  Michiel E Adriaens (2) (3)
  Amelie Gormand (2) (4)
  Oliver E Sturm (2) (5)
  Walter Kolch (6)
  David R Gilbert (2) (7)
  
• 刊名：BMC Systems Biology
• 出版年：2009
• 出版时间：December 2009
• 年：2009
• 卷：3
• 期：1
• 全文大小：2562KB
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• 作者单位：Richard J Orton (1) (2)
  Michiel E Adriaens (2) (3)
  Amelie Gormand (2) (4)
  Oliver E Sturm (2) (5)
  Walter Kolch (6)
  David R Gilbert (2) (7)
  
  1. Institute of Comparative Medicine, Faculty of Veterinary Medicine, University of Glasgow, Glasgow, G61 1QH, UK
  2. Bioinformatics Research Centre, Department of Computing Science, University of Glasgow, Glasgow, G12 8QQ, UK
  3. Department of Bioinformatics - BiGCaT, Maastricht University, Maastricht, The Netherlands
  4. Department of Experimental Medical Science, Faculty of Medicine, Lund University, Lund, Sweden
  5. St Jude Children's Research Hospital, Memphis, Tennessee, TN, 38105, USA
  6. Beatson Institute for Cancer Research, Garscube Estate, Glasgow, G61 IBD, UK
  7. School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, Middlesex, UB8 3PH, UK
  

文摘

Background The Epidermal Growth Factor Receptor (EGFR) activated Extracellular-signal Regulated Kinase (ERK) pathway is a critical cell signalling pathway that relays the signal for a cell to proliferate from the plasma membrane to the nucleus. Deregulation of the EGFR/ERK pathway due to alterations affecting the expression or function of a number of pathway components has long been associated with numerous forms of cancer. Under normal conditions, Epidermal Growth Factor (EGF) stimulates a rapid but transient activation of ERK as the signal is rapidly shutdown. Whereas, under cancerous mutation conditions the ERK signal cannot be shutdown and is sustained resulting in the constitutive activation of ERK and continual cell proliferation. In this study, we have used computational modelling techniques to investigate what effects various cancerous alterations have on the signalling flow through the ERK pathway. Results We have generated a new model of the EGFR activated ERK pathway, which was verified by our own experimental data. We then altered our model to represent various cancerous situations such as Ras, B-Raf and EGFR mutations, as well as EGFR overexpression. Analysis of the models showed that different cancerous situations resulted in different signalling patterns through the ERK pathway, especially when compared to the normal EGF signal pattern. Our model predicts that cancerous EGFR mutation and overexpression signals almost exclusively via the Rap1 pathway, predicting that this pathway is the best target for drugs. Furthermore, our model also highlights the importance of receptor degradation in normal and cancerous EGFR signalling, and suggests that receptor degradation is a key difference between the signalling from the EGF and Nerve Growth Factor (NGF) receptors. Conclusion Our results suggest that different routes to ERK activation are being utilised in different cancerous situations which therefore has interesting implications for drug selection strategies. We also conducted a comparison of the critical differences between signalling from different growth factor receptors (namely EGFR, mutated EGFR, NGF, and Insulin) with our results suggesting the difference between the systems are large scale and can be attributed to the presence/absence of entire pathways rather than subtle difference in individual rate constants between the systems.