26A. Tracking the origin of childhood cancer

Although aging is the biggest risk factor for getting cancer, children can also develop cancer. In fact, the incidence of some cancers is higher in children compared to young adolescents. We aim to address this paradox by pinpointing the rate-limiting steps underlying the genesis of childhood leukaemia and lymphoma. To achieve this aim, we have two objectives:

1. Identify the processes that contribute to the development of childhood cancer by in-depth mutation analyses.
2. Pinpoint the clonal origin of childhood leukaemia and lymphoma during the development of the hematopoietic system by retrospective lineage tracing

In our research group, we have pioneered the development of methods to study mutation accumulation in single human stem cells. Using this approach, we have constructed a baseline for mutation accumulation in healthy blood during life and a developmental lineage tree of native human haematopoiesis. We will apply our method and use our unique knowledge on mutagenesis in the human hematopoietic system to track down the origin and mutational complexity of childhood leukaemia and lymphoma. For this, we will examine bone marrow and or tumor biopsies of childhood cancer patients at the time of diagnosis. Instead of identifying novel genetic cancer drivers, we will focus on studying all (mostly passengers) mutations to explore processes that cause initiation and progression of pAML.

We have previously demonstrated that somatic mutations can be used to trace the clonal origin of childhood leukaemia, time the occurrence of crucial events in the life history of the cancer and identify causative processes. We will extent these findings by characterizing the genomes of hematopoietic cells of a cohort of children with various leukemia and lymphoma subtypes. We will validate our findings by mimicking mutation profiles in vitro by combining mutagenic exposure and CRISPR/Cas9 gene editing in umbilical cord blood-derived stem cell cultures.

Necessary skills for this position:

• Bioinformatics skills; knowledge on R and Python