26B. The mutational consequences of cancer treatment and the evolution towards therapy-related leukaemia

Childhood cancer survivors are confronted with a variety of chronic health conditions because of their life saving therapy. Most chemotherapeutic drugs act by fatally damaging the DNA or blocking the replication thereof. However, noncancerous cells are also damaged by treatment, which can result in the accumulation of DNA mutations in normal tissues with potentially adverse effects later in life. To minimize these adverse late effects, cancer treatment should be adapted to reduce mutagenicity for normal tissues while the cytotoxicity for cancer cells remains unaltered. However, the long-term mutational consequences of childhood cancer treatment in normal cells are currently unknown. The overall aim of this project is to study the mutational effects of chemotherapy in normal tissues of children to develop novel treatment strategies aimed at minimizing adverse late effects. To achieve this, our objectives are:

1. Determine the in vivo mutagenicity of each chemotherapeutic drug.
2. Define genetic markers to identify children with increased sensitivity.

Our research group has pioneered the development of methods to characterize lifelong mutation accumulation in normal human stem cells. We have obtained unique knowledge on how mutations accumulate during human life in various tissues, which is crucial to study the additive mutational effects of chemotherapy in children. We will systematically assess the mutational consequences of chemotherapy exposure in the haematopoietic system of children treated for cancer. For this, we will analyse by whole genome sequencing individual haematopoietic cells isolated of the same patient before and after exposure to chemotherapy and perform in-depth mutational analyses to pinpoint additive mutagenic effects of cancer treatment. We will use the observed somatic mutations to examine the effects of treatment on the clonal composition and dynamics of the entire blood tissue. By systematically analysing genome-wide mutational patterns resulting from such interaction, we aim to define genetic risk markers.

Necessary skills for this position:

• Bioinformatics skills; knowledge on R and Python