Objective
Defining and characterising the defective genes in hereditary cancer syndromes has advanced our understanding of cellular function and disease mechanisms. Interestingly, some of these genes have been directly implicated in metabolic dysregulation, thus providing a link between genetic mutation and altered metabolism in cancer. One such syndrome, hereditary leiomyomatosis and renal cell cancer (HLRCC), is caused by germline mutations in the FH gene encoding the Krebs cycle enzyme fumarate hydratase. The aim of this proposal is to define pathways disrupted in HLRCC and within these to determine specific points, susceptible to genetic or chemical intervention, from which therapies might be derived to treat or prevent tumourigenesis. First, we will assess candidate mechanisms for FH-associated tumourigenesis which we have identified through recent studies, encompassing enzyme inhibition, protein modification, anti-oxidant signalling and altered energy metabolism. Secondly, to identify novel RCC associated mutations and clarify their relevance in the evolution and metabolism of RCC, transposon-based mutagenesis will be employed to induce RCCs in both wildtype and Fh1-deficient mice. Analyses will include histological analysis, metabolite profiling, and high resolution sequencing. Candidate genes will then be screened in relevant human RCC and pre-malignant lesions. Finally, a synthetic lethality screen will be performed in parallel with metabolic profiling to identify the pathways that are critical for the growth of FH-null cells. Taken together it is envisaged that this work will not only provide insights into this rare but aggressive disease but also inform on potential targets for intervention in more common cancers that are also characterised by metabolic dysregulation.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural sciencesbiological sciencesgeneticsmutation
- medical and health sciencesclinical medicineoncology
- natural sciencesbiological sciencesbiochemistrybiomoleculesproteinsenzymes
You need to log in or register to use this function
Call for proposal
ERC-2012-StG_20111109
See other projects for this call
Funding Scheme
ERC-SG - ERC Starting GrantHost institution
EH8 9YL Edinburgh
United Kingdom