Analysis methods for the proteogenomics of oesophageal cancer.
Poster: 04 SEPTEMBER 2017 [Closing date 1st October]
We invite enquiries from qualified and highly motivated applicants for a 3 year PhD studentship comprising of tuition fees (Home/EU) and stipend.
Oesophageal cancer is a priority area of research. While only the 14th most common cancer in the UK, it is the 6th most common cause of cancer death. This disparity is an indicator of the generally poor survival rates for the disease in spite of a substantial research effort. Much work from projects such as TCGA and ICGC has gone into identifying molecular subtypes of oesophageal (and more general gastric) cancers in order that targeted treatment options might be identified. While considerable numbers of oesophageal cancers have had their genomes sequenced, far fewer have also undergone quantitative proteomic profiling, despite this being the key molecular phenotype by which we should assess cases.
Cell-lines, organoids, and other disease models have a vital role to play in understanding the biology of oesophageal cancer and in developing potential treatments. Inferences from experiments using such models are only as valid as the model is truly representative of the diseases. A number of cell-lines have been profiled for their genomic sequence, but not their proteome.
Supervised by Professor Andy Lynch and in collaboration with Professor Russell Petty (University of Dundee), the project will analyse proteomic data from oesophageal cancer models, placing the results in the context of current knowledge. The successful candidate will apply existing approaches, and develop new methods, for the integration of proteomic and genomic data. The degree to which the disease models represent clinical cases, and the proportion of cases represented will be assessed with a view to developing a framework to predict the extent to which the molecular effects of interventions on the disease model can be expected to translate to clinical samples. In this manner the disease models as tools for understanding clinically-defined disease subtypes will have maximum utility.
Applications are encouraged from graduates with backgrounds in a numerate discipline (e.g. bioinformatics, statistics, mathematics, or computer science). The ideal candidate will have an interest in genomics and an enthusiasm for learning about biology. A Masters degree in statistics or similar would be desirable. Some experience of working with genetic data would be desirable but not essential. Experience of coding and scripting in R (and ideally Bioconductor) is desirable.
How to apply
For further details on the project and informal enquiries please contact to Prof. Andy Lynch (email@example.com ) with a CV and a covering letter. Start date: 1 November 2017 or as soon as possible after then.