Home

Welcome to the SYS-MEL Project 


Project at a glance
SYS-MEL is focused on developing prognostic and predictive tests for melanoma, the most aggressive form of skin cancer. Currently, there are a paucity of tests to inform clinicians whether primary melanomas will spread to other organs and cause death (prognostic test), or if the patient will respond to standard chemotherapy (predictive tests). The SYS-MEL Consortium will tackle this pressing clinical and commercial need by investigating several key molecular pathways that drive melanoma progression, with a focus on epigenetically-regulated targets, the apoptosis cascade and kinase signalling.

Worldwide figures show that there were 130,000 incidences of melanoma in 2008, whilst 66,000 died from the disease. These upward trends are worrying, as malignant melanoma is one of the most difficult cancers to treat, due to its ability to spread quickly and its resistance to standard chemotherapeutic agents. In order to counteract this trend, targeted therapies that inhibit melanoma metastasis are required. The aim of SYS-MEL is to identify and validate prognostic and predictive biomarkers for melanoma, and to model the predictive value of these biomarkers in determining the efficacy of melanoma therapies. The central objective is to bring together four European academic institutes and two SMEs to develop prognostic/predictive biomarker assays for melanoma. Core areas of interest are epigenetics, signalling pathways in melanoma and systems biological approaches for predicting chemotherapy responses.


SYS-MEL has three main elements:
1) Epigenomic and protein expression analysis of melanoma tissue, to validate an epigenomic signature initially identified in the FP7-funded programme, Target-Melanoma.

2) In silico modelling and prediction of patient responses to decarbazine DTIC using both in vitro analysis of apoptotic pathways and a novel systems biology approach, incorporating mathematical systems modelling, quantitative biochemistry and cell biology.

3) Investigating the components of the P-Rex1 pathway that are involved in driving the migration of melanoblast cells, and thus the progression of metastasis, incorporating a computational system tailored to model complex signalling pathways.


These approaches will enable us to identify prognostic and predictive biomarkers for melanoma, and to develop a powerful computational modelling approach to predict disease progression and patient responses to treatment.