Chronic myelomonocytic leukemia (CMML) is a rare and aggressive blood cancer that affects the production of certain white blood cells called monocytes. Patients with CMML often have a poor prognosis, with limited treatment options and a high risk of progression to more severe forms of leukemia. Despite recent advances in our understanding of the molecular changes associated with CMML, the underlying genetic causes of the disease remain largely unknown. This lack of knowledge has hindered the development of effective prevention, diagnosis, and treatment strategies for CMML.
Our research project aims to address this challenge by leveraging the resources of the UK Biobank, which is a large-scale biomedical database containing genetic and health information from 500,000 individuals. By comparing the data of individuals with CMML to those of healthy individuals in the UK Biobank, we hope to identify specific variations that may contribute to the development and progression of CMML.
To achieve this goal, we will employ state-of-the-art computational methods to analyze the vast amounts of genetic and health data available in the UK Biobank. This will involve using machine learning algorithms to identify patterns and associations between genetic variations and CMML risk, as well as developing predictive models that can assess an individual’s likelihood of developing the disease based on their genetic profile, biomarkers, proteins, and other health factors.
The expected duration of our research project is three years. During this time, we will work closely with clinical collaborators and patient advocacy groups to ensure that our findings are clinically relevant and can be translated into meaningful benefits for patients with CMML.