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

This article has been made available through the Brunel Open Access Publishing Fund - Copyright @ 2009 Orton et al.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.This work was funded by the Department of Trade and Industry (DTI), under their Bioscience Beacon project programme. AG was funded by an industrial PhD studentship from Scottish Enterprise and Cyclacel

WINROP identifies severe retinopathy of prematurity at an early stage in a nation-based cohort of extremely preterm infants

OBJECTIVE: To evaluate the ability of a postnatal weight-gain algorithm (WINROP) to identify sight-threatening retinopathy of prematurity (ROP type 1) in a nation-based extremely preterm infant cohort. METHODS: This study enrolled all 707 live-born extremely preterm (gestational age [GA] <27 weeks) infants, born 2004-2007 in Sweden; the Extremely preterm Infants in Sweden Study (EXPRESS). WINROP analysis was performed retrospectively in 407 of the infants using weekly weight gain to assess the preterm infant's risk of developing ROP type 1 requiring treatment. GA, birthweight (BW), and weekly postnatal weight measurements were entered into WINROP. WINROP signals with an alarm to indicate if the preterm infant is at risk for ROP type 1. RESULTS: In this extremely preterm population, WINROP correctly identified 96% (45/47) of the infants who required treatment for ROP type 1. The median time from alarm to treatment was 9 weeks (range, 4-20 weeks). CONCLUSIONS: WINROP, an online surveillance system using weekly weight gain, identified extremely preterm infants at risk for ROP type 1 requiring treatment at an early stage and with high sensitivity in a Swedish nation-based cohort