This project is offered as part of the University of Dundee 4-year MRC DTP Programme “Quantitative and Interdisciplinary approaches to biomedical science”. This PhD programme brings together leading experts from the School of Life Sciences (SLS), the School of Medicine (SoM) and the School of Science and Engineering (SSE) to train the next generation of scientists at the forefront of international science. The outstanding biomedical research at the University of Dundee was recognised by its very high rankings in REF 2014, with Dundee rated as the top University for Biological Sciences in the UK. A wide range of projects are available within this programme crossing exceptional strengths in four key areas: Infection and Disease; Responses to Cellular Stresses; Development, Stem Cells and Neurobiology; and Big Data and Translation. All students on this programme will receive training in computational biology, mathematical biology and statistics to equip with the quantitative skills in tackling complex biological questions. In the 1st year, students will carry out 3 rotation projects prior to selection of the final PhD project.
This PhD project involves a multidisciplinary analysis of oesophageal cancer using proteomic and genomic data combined with computational methods, including deep learning. The project will generate and analyse quantitative proteomic data, using mass spectrometry, from both patient derived oesophageal tumour and matched healthy tissue samples, together with analysis of a collection of oesophageal cancer cell lines, with matched genomic data. The aims are to identify features in the proteo-genomic data that can be used to predict clinical outcomes and responses to drug treatments, leading to better outcomes for oesophageal cancer patients. The project will leverage major progress in an ongoing collaboration between the two supervisors in this area of studying oesophageal cancer using proteo-genomic methods, including their participation in the international proteogenomic cancer moonshot programme. The project will benefit from the expertise of the supervisors in the areas of quantitative proteomics, computational methods for big data analysis, including deep learning and in clinical oncology and oesophageal cancer.
Recent work from the labs can be found in the following references:
“Proteomic analysis of cell cycle progression in asynchronous cultures, including mitotic subphases, using PRIMMUS”; Ly T, Whigham A, Clarke R, Brenes-Murillo AJ, Estes B, Madhessian D, Lundberg E, Wadsworth P and Lamond AI. eLife (2017) doi:10.7554/eLife.27574. PMID:29052541 / PMCID:PMC5650473
“Proteome-wide analysis of protein abundance and turnover remodelling during oncogenic transformation of human breast epithelial cells”; Ly T, Endo A, Brenes A, Gierlinski M, Afzal V, Pawellek A and Lamond AI. Wellcome Open Res. (2018) doi: 10.12688/wellcomeopenres.14392.1. PMID: 29904729 / PMCID:PMC5989152
“Gefitinib and EGFR Gene Copy Number Aberrations in Esophageal Cancer”; J Clin Oncol. 2017 Jul 10;35(20):2279-2287. doi: 10.1200/JCO.2016.70.3934. Epub 2017 May 24.
Petty RD, Dahle-Smith A, Stevenson DAJ, Osborne A, Massie D, Clark C, Murray GI, Dutton SJ, Roberts C, Chong IY, Mansoor W, Thompson J, Harrison M, Chatterjee A, Falk SJ, Elyan S, Garcia-Alonso A, Fyfe DW, Wadsley J, Chau I, Ferry DR, Miedzybrodzka Z.
“Machine learning and data mining frameworks for predicting drug response in cancer: An overview and a novel in silico screening process based on association rule mining”; Pharmacol Ther. 2019 Jul 30:107395. doi: 10.1016/j.pharmthera.2019.107395. [Epub ahead of print]
Vougas K, Sakellaropoulos T, Kotsinas A, Foukas GP, Ntargaras A, Koinis F, Polyzos A, Myrianthopoulos V, Zhou H, Narang S, Georgoulias V, Alexopoulos L, Aifantis I, Townsend PA, Sfikakis P, Fitzgerald R, Thanos D, Bartek J, Petty R, Tsirigos A, Gorgoulis VG.