4-year PhD Studentship: Identifying novel links between Type 2 Diabetes, Obesity and Cancer.

It is known that there is an exponential increase in the incidence of obesity, type 2 diabetes (T2D), and cancer particularly in recent decades. These trends reflect the links between obesity, T2D, and cancer, and in most cases, the increase in obesity and T2D correlate with a rise in the risk of and death from cancer. The risk of these diseases is generally associated with the older generation. However, a rise in obesity and T2D in younger age groups is mirrored by a rise in risk and death from cancer prematurely[1, 2]. This represents a significant public health issue and cost to society, but currently there is a paucity of information pinpointing the exact ways in which these conditions are linked. This proposal aims to compare known information about T2D, obesity and cancer, to find aspects that overlap. These will be investigated to pinpoint novel ways in which T2D/obesity specifically affects the behaviour of cancer cells and ways to prevent these interactions will be explored. This work may lead to cancer prevention and intervention strategies specifically for those with T2D/obesity.

Aims and Objectives

Hypothesis

The identification of novel links that exist between type 2 diabetes, obesity and cancer will provide novel approaches for cancer prevention and intervention strategies specifically designed for those with T2Ds who are overweight.

Objective 1: This aim proposes a bioinformatic approach to determine additional risk factors common to diabetes, obesity, and cancer for validation.

Objective 2: To use in vitro models to determine if identified target is a plausible biologic link between diabetes, obesity, and cancer. Any additional factors uncovered will be fed back into the ‘in silico’ approach outlined in objective 1.

Methodology

This project uses an array of different techniques and will provide comprehensive training for a PhD student. The student will be trained in bioinformatics, so they can apply methods and software tools as a way of interpreting biological data. Publicly available datasets will be accessed to perform this work supervised by Richard Bryan and Richard Martin, who have published extensively in this field. The student will be trained in key platforms and methodologies developed at the University of Bristol. For example, MR-Base and Mendelian randomization. The student will also use pre-clinical in vitro cell lines exposed to conditions associated with T2D/obesity (altered levels of glucose, insulin/IGF-I and cytokines) in which they will manipulate the identified targets (e.g gene silencing). A range of ‘normal’ and malignant cell lines (breast and colorectal initially) will be used that reflect progression of the disease. mRNA and protein will be measured using qPCR and western immunoblotting and secreted proteins assessed using ELISA and/or radioimmunoassay. Tritiated thymidine incorporation and crystal violet assays will be used to monitor alterations in cell growth, Muse Cell Analyser™ flow cytometry kits and clonogenic assays for survival and a HoloMonitor® live cell imaging system and trans-well chambers for migration and invasion.

Keywords

cancer, diabetes, obesity, metabolism, bioinformatics, genetic epidemiology, cell biology.

How to apply for this project

This project will be based in Bristol Medical School - Translational 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