Machine learning for functional reconstruction and analysis of biochemical interactions controlling colorectal cancer phenotypes

The 60% expected increase in deaths from CRC by 2030, which is already the fourth leading cause of cancer-related deaths in the world, raises a pressing need for better clinical tools for CRC patient stratification and treatment.Context-specific network inference (CoSNI) is a tool capable of integrating various functional genomics data to make predictions that capture context-specific molecular features to construct biochemical networks.

Developed into an R package capable of handling cancer datasets using modified data pipelines generated in this project, it was applied to integrate polyomics data focused on CRC to capture molecular features specific to tumour stage and subtype. Networks for the following CRC contexts, stage II, stage III, CMS2 and CMS3, were successfully produced using CoSNI. These contexts were chosen for their influence on clinical decisions centred on prognosis and treatments.The four constructed networks were analysed using the Marisa dataset to interpret the network models using overall survival and response to chemotherapy to understand context-specific molecular mechanisms controlling these endpoints.