Machine Learning for Discovery of Patient Journey-Wide Phenotypes and Colorectal Cancer Stratification

Colorectal cancer is the second highest cause of cancer mortality, associated with >880,000 deaths per annum worldwide. This project seeks to develop novel approaches for stratification of colorectal cancer patients in order to help inform clinical decision-making. For example, while a proportion of stage II colorectal cancer patients benefit from chemotherapy, it can be challenging to identify which specific patients will benefit [Kannarkatt et al. Journal of Oncology Practice 2017]. Cutting-edge informatics techniques will be applied to large datasets, including substantial linked clinical and demographic data, in order to discover fingerprints of individual characteristics that define new phenotypes across the patient journey. These data-driven patient phenotypes may include factors, for example relating to lifestyle, that influence the molecular processes driving cancer progression. Therefore discovery of patient phenotypes may define new cohorts for development of novel phenotype-specific molecular stratification approaches.

Work during this four year studentship will be primarily based in the Overton group at Queen's University Belfast and associated with the Health Data Research UK Wales and Northern Ireland substantive site. The studentship includes six months to be spent at the LifeArc Centre for Diagnostics Development in Edinburgh, an ISO13485 certified environment. The student will benefit from LifeArc's considerable diagnostics development expertise, helping to ensure the anticipated novel diagnostic software is competent for potential clinical use.