Every year, more than 60,000 individuals in Sweden are diagnosed with cancer. Earlier detection, improved risk prediction, and new prevention strategies are thus key goals for public health. Known risk factors for developing cancer, including genetics, physical activity, diet, and obesity, affect the type and amount of different small molecules, called metabolites, circulating in our blood.
The use of metabolomics data
But, how these changes impact cancer development, and if they reflect potential targets for cancer prevention, is poorly understood. In addition, early detection and treatment of cancer can reduce mortality, but predicting if a particular individual will develop cancer is challenging. However, emerging results are beginning to show how information about metabolites measured in blood samples, so called metabolomics data, can be used for understanding biological pathways relevant to developing cancer and improving risk prediction for developing cancer, without the need for invasive or time consuming clinical investigations.
Machine learning and causal inference
Hannah Brooke aims to combine high-quality metabolomics and questionnaire data from the EpiHealth cohort with corresponding data in the Swedish Infrastructure for Medical Population-Based Life-Course and Environmental Research (SIMPLER) analyses will be replicated using data from UK Biobank where possible. The data will be analysed using cutting-edge methods in machine learning and causal inference to provide fundamental and novel insights into the biology behind cancer development, improved prediction of cancer risk, and potential targets for cancer prevention.
