Assessment of clinical discriminatory performance of CRC pathological staging and related uncertainty using omic data and Bayesian methodology

Colorectal cancer (CRC) is the second most common cause of cancer death in UK. Whilst pathological staging stratifies prognostic groups, it does not perform well in categorising poor/ good prognosis tumours at individual level, and the genetic predisposition to CRC prognosis has not been extensively investigated. In this project we will combine data from the UK Biobank and Scottish Colorectal Cancer Study (SOCCS), and will fit robust statistical models to investigate the association of genotype with mortality, adjusting for age, gender and stage of cancer and accounting for the tumour MSI status. The clinical variation within pathological staging groups with respect to prognostic categorization and chemotherapy administration at individual level will also be addressed. We aim to appraise the clinical usefulness of identified genetic, socio-demographic and biomarker risk factors in discriminating high and low risk tumours within a given stage, and also help inform the decision of chemotherapy administration, given side effects and cost implications. We will assess statistical measures related to predictive ability and prognostic accuracy using methodology relying on posterior predictive checking and latent residual processes, under a Bayesian statistical approach that allows for the uncertainty associated with predictions to be quantified. The application of such methodology in the context of this project is novel and at the forefront of current practice.

The project will be funded by a DTP in Precision Medicine (http://www.ed.ac.uk/medicine-vet- medicine/news-events/latest-news/mrc-award-for-precision-medicine-doctoral-training) and will be supervised jointly by the Usher Institute of Population Health Sciences and Informatics, University of Edinburgh and the School of Mathematical and Computer Sciences, Heriot-Watt University.

Training outcomes
The student will be trained in the core disciplines of informatics, data science, mathematics and statistics placed in the CRC context and will develop skills related to novel analytical and quantitative methods underpinning modern data-driven approaches. They will also get further cross-disciplinary biomedical science training to determine knowledge value.

Application
This MRC DTP programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

You can apply here via the University of Glasgow: http://www.gla.ac.uk/research/opportunities/howtoapplyforaresearchdegree/
Within the application, at the programme of study search field option, please select ‘MRC DTP in Precision Medicine’.

Please note that, in step 6 within the online application process, you are asked to detail supervisor/project title information. Please ensure that you clearly detail this information from the information provided within this abstract advert. Within the research area text box area, you can also add further details if necessary.

Please ensure that all of the following supporting documents are uploaded at point of application:
• CV/Resume
• Degree certificate (if you have graduated prior to 1 July 2016)
• Language test (if relevant)
• Passport
• Personal statement
• Reference 1 (should be from an academic who has a knowledge of your academic ability from your most recent study/programme)
• Reference 2 (should be from an academic who has a knowledge of your academic ability)
• Transcript

For more information about Precision Medicine at the University of Edinburgh, visit http://www.ed.ac.uk/medicine-vet-medicine/postgraduate/research-degrees/phds/precision-medicine