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Click here to search FindAPhD.com for PhD studentship opportunitiesAbout the Project
Colorectal cancer (CRC) is one of the most common adult cancers in the UK(1). Along with lifestyle interventions and screening (i.e. colonoscopy for early detection of colorectal polyps or CRC), chemoprevention strategies (i.e. the use of pharmacological intervention to arrest or reverse the process of carcinogenesis) may offer an important strategy to reduce the burden of CRC, particularly in groups at elevated lifetime risk of this disease (e.g. individuals with Lynch syndrome, familial adenomatous polyposis)(2).
Recent large-scale genome-wide association studies (GWAS) of circulating proteins (i.e. targets of many medications) have identified thousands of protein quantitative trait loci (pQTLs) that influence protein levels(3-7). These variants can be used in a Mendelian randomization (MR) framework to estimate the causal effects of potential chemoprevention targets on colorectal cancer(8). Robust identification of circulating proteins that causally influence risk or subsequent progression of cancer can then be used inform development of pharmacological interventions for cancer prevention and treatment in high-risk groups.
Aims and objectives
The overall aim of this project is to identify novel chemoprevention agents for colorectal cancer. The specific objectives of this project are as follows:
- Identify candidate chemoprevention agents for CRC by performing an updated systematic review of the epidemiological and laboratory literature.
- Appraise causality between candidate chemoprevention agents and CRC using MR.
- Identify potential mechanisms underpinning effects of chemoprevention agents on CRC using colon tissue gene expression data in the BarcUVa-Seq study.
- Perform “triangulation” analysis of findings from Objective 2 and 3 analyses using the UK Biobank study and the seAFOod Polyp Prevention trial.
Methodology
A comprehensive systematic review of epidemiological and laboratory studies linking candidate chemoprevention targets (e.g. circulating proteins, approved medications) to CRC will be performed using EMBASE, PubMed, and Web of Science along with data mining tools (e.g. EpiGraphDB). For all candidate chemoprevention targets, genome-wide significant (P<5x10-8) single-nucleotide polymorphisms associated with circulating protein concentrations of interest (or protein targets of approved medications) will be used to develop genetic instruments for these targets to estimate their causal effect on CRC(3-7). To identify potential mechanisms underpinning the effect of these targets on CRC, genetic risk scores will be constructed to proxy these targets to test their association with gene expression profiles using colon tissue samples from BarcUVa-Seq(9). Top findings from MR and gene-expression analyses (i.e. those robust to multiple-testing correction), will then be “triangulated” using circulating protein measures in the UK Biobank and tissue-level “omic” measures in the seAFOod Polyp Prevention trial(10,11).
This project will provide comprehensive and advanced training in genetic epidemiology and cancer, specifically MR applied to molecular traits and cancer biology, and experience writing up findings for publication and presentation. You will join a large cohort of fellow doctoral students and be part of a vibrant, intellectually generous and supportive Department.
How to apply for this project
This project will be based in Bristol Medical School - Population Health Sciences in the Faculty of Health Sciences at the University of Bristol.
Please visit the Faculty of Health Sciences website for details of how to apply
Funding Notes
The University of Bristol PGR scholarship pays tuition fees and a maintenance stipend (at the minimum UKRI rate) for the duration of a PhD (typically three years but can be up to four years).
References
2. Umezawa et al. Cancer Sci, 2019. doi: 10.1111/cas.14149
3. Davey Smith et al. IJE, 2003. doi : 10.1093/ije/dyg070
4. Schmidt et al. Nature Communications, 2020. doi: 10.1038/s41467-020-16969-0
5. Folkersen et al. Nature Metabolism, 2020. doi: 10.1038/s42255-020-00287-2
6. Pietzner et al. Nature Communications, 2020. doi: 10.1038/s41467-020-19996-z
7. Gilly et al. Nature Communications, 2020. doi: 10.1038/s41467-020-20079-2
8. Sun et al. Nature, 2018. doi: 10.1038/s41586-018-0175-2
9. Diez-Obrero et al. Cell Mol Gastroenterol Hepatol, 2021. doi:10.1016/j.jcmgh.2021.02.003.
10. Collins et al. Lancet, 2012. doi: 10.1016%2FS0140-6736%2812%2960404-8
11. Hull et al. Lancet, 2018. doi: 10.1016/S0140-6736(18)31775-6

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