Gene variant driven sub-stratification of breast cancer patients.

Current breast cancer diagnosis relies on binary (positive/negative) values for estrogen/progesterone hormone receptors (HR), and HER2 receptor values. Together, these form the hormone receptor status (HRS), which is closely linked to clinical breast cancer treatment decisions. It is increasingly recognised that breast cancer is an extremely heterogenous disease which in reality represents many varied subtypes. As a result of treatment decisions based around a binary classification system, patient outcomes are highly varied, difficult to predict, and there are limited means to personalise chemotherapy. Increasingly, the field is looking for methods to move beyond hormone receptor status to further stratify patients in a more granular fashion, one promising way to do this is using gene variants.

The aim of this current project is to develop a computational model to link genetic variants to the sub-stratified phenotypic hormone expression and resulting tumour microenvironment. With our industrial partner Onkolyze, we will develop a system that will allow scientists and clinicians to classify breast cancer into "genetic subtypes" and potentially design personalised treatment strategies with higher efficacy for these subtypes.

The student will develop a AI model that can link gene variants to hormone receptor percentage expression, which is currently characterised during existing clinical pathology workflow. This model will predict the resultant phenotypes and possible drug-cell interactions of these phenotypes. Our models utility will be validated in patient-derived cell lines and patient-derived microenvironments from the MCRC biobank and collaborators, for imaging modality analysis. This will enable the prediction of treatment outcomes based on genetic variants found in patient tissue at a cellular level without deviating significantly from existing clinical workflow.

The student will receive a broad interdisciplinary skills training in bioinformatics applied to personalised oncology; specifically, they will be trained in state-of-the-art techniques from AI/machine learning, neural networks, bioinformatics, and statistics. In addition, via the validation steps they will learn in-vitro analytical, technical and cell biology methods including cell culture and immunofluorescence microscopy. The student will gain a strong theoretical understanding in the relevant background theory (e.g. cell cycle, DNA replication, chemotherapy approaches, clinical workflow, and cancer). The student will also develop an understanding of a multidisciplinary approach to modelling and collaboration via our industrial partner's existing on-going collaborations and those at UoM.

At the end of the PhD the student will have a body of work suitable for a high-quality thesis, multiple high impact publications, a strong interdisciplinary skill set and industry links, making them extremely competitive for posts in pharma/biotech or academia.