Using machine learning to understand the effects of mutations on the epigenome in cancer
Dr Duncan Sproul
Epigenetic dysfunction is a fundamental hallmark of cancer that is associated with the repression of tumour suppressor genes such as BRCA1. However, we don’t understand how these potential epimutations occur. We have shown that epigenetic marks are strongly programmed by the genome meaning that mutations can change the epigenome. This project aims to use such mutations as tools to understand how genes become repressed in cancer by combining machine-learning and genome-editing. This combination of state-of-the-art approaches will ensure we will be able to determine mutations affect the epigenome, identify the proteins responsible and understand how these interactions affect gene transcription and the pathogenesis of breast cancer.