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2018 - Spaniards have their daily siesta, Germans like sausages and Belgians love beer. Stereotypes can certainly be misleading, just like judging a cell by its membership to a particular cell type, the so-called population-based analysis. Nowadays, we know that tumors contain a lot of different cells, including tumor cells with different mutations and also normal cells. In the era of single-cell DNA and RNA analysis (single-cell sequencing), we have the exquisite opportunity to study each individual cell with unprecedented resolution. Acute lymphoblastic leukemia (ALL) is a type of cancer in which the bone marrow makes too many lymphocytes (a type of white blood cell). ALL is a success story in pediatric oncology, with the majority of children surviving the disease based on optimized chemotherapy treatment. For adult ALL patients, however, the prognosis (chance of recovery) remains generally poor, with high frequency of relapse (disease recurrence). I hypothesize that relapse can be predicted earlier in adult ALL patients if we get a better picture on the different leukemia cells that are present at diagnosis and if we get more information on the sensitivity of these different cells to chemotherapy. In this project, I will use recently developed single-cell analysis techniques to address these questions. In collaboration with the hematology department of UZ Leuven, I will analyze adult ALL blood and bone marrow samples at diagnosis, at regular intervals during treatment, and eventually at relapse. The analysis of the DNA and RNA of thousands of cells in these samples will give a view on the complexity of these leukemias at diagnosis, their evolution during treatment and the re-growth of a resistant leukemia cell at relapse. With all this information available, I will be able to track the fate of each type of leukemia cell and detect the origin of the relapse clone. This approach will provide temporal information about how leukemia develops, the exact types of cells that compose it and the sensitivity of the different cells to the therapy. This information will help to determine whether the level of heterogeneity can be linked with prognosis as well as potentially identify subclones that correlate with drug resistance and predict the development of relapse. Consequently, the results of this project will set the basis for improved risk-stratification methods based on individualized patient’s molecular profiles and will ultimately permit the development of novel risk-adapted therapies for adult ALL patients.