25A. Exploiting genetic interactions for modelling regulatory networks in pediatric cancer

Genetic interactions between mutated genes play an important role in cancer development and treatment. Little is however known how genetic interactions relate to cellular processes and shape regulatory networks involved in pediatric cancer development. A mechanistic understanding of these interactions is also highly needed to design effective and targeted treatments. Not only do these interactions provide crucial information about which mutations are co-occurring or mutually exclusive. They also reflect pathway activity and interactions between these pathways.  This information in turn can be exploited for understanding the regulatory mechanism involved in cancer development and aid in the design of targeted therapies. Using cancer genome data of over 2,500 pediatric cancer cases, we have generated the most comprehensive map of genetic interactions in pediatric cancer.

To advance our understanding of the biological processes involved, a number of these interactions warrant further investigation and extensive characterization of the underlying regulatory network. Here, we intend to systematically investigate and model regulatory networks underlying several genetic interactions. First, we will select several candidate pairs from the genetic interaction map generated previously. Second, by introducing the single and double mutations in organoid or cancer cell lines followed by single-cell RNA-sequencing, we will measure and link the effect of the single and double mutations on mRNA expression for individual cells. Third, by extending a previously developed computational framework we will explore a whole spectrum of potential regulatory models that can explain the observed behavior. Through this modeling approach, we expect to provide a much clearer insight into the regulatory networks that underly genetic interactions in pediatric cancer. This is crucial not only to gain understanding of the biological processes involved and in which way these are involved in tumor development, but also for future design and applications of targeted therapies that exploit synthetic lethal relationships.

Necessary skills for this position:

• Background in bioinformatics or computational biology