25B. Untangling somatic structural variation in pediatric cancers

Structural variants are known to drive cancer initiation and progression and are important for diagnostic purposes in at least 30% of cancers. For many of these structural variants it remains difficult to detect and interpret their functional consequences, both of which are crucial for biological understanding and clinical utility. More effective structural variant analysis in cancer genomes will increase the molecular diagnostic yield, which in turn can lead to more effective and personalized treatment options.

Despite the success of short-read whole genome sequencing, specific sizes and types of structural variation remain difficult to detect in tumor samples. These undetected variants can now be identified using long-read sequencing, and include events known to have a role in cancer. Additional advantages of long-read sequencing include its suitability for the detection of complex structural variants and phasing of small variants. Recent developments and improvements in accuracy of long-read sequencing make detecting somatic structural variants in tumor genomes possible and will result in new discoveries into the role of structural variants in cancer.

Specifically, we will focus on the use of long-read sequencing to solve key issues currently hampering structural variation research in cancer. Namely if long-read sequencing is a viable genome wide approach for phasing mutations, detection of viral insertions and retrotranspositions, and can long read sequencing increase our understanding of mechanisms driving complex structural variation.

This project will investigate the impact of long-read sequencing on pediatric cancer research. Knowledge gained during this project will improve our understanding of structural variation in cancer genomes, including how individual DNA breakpoints are linked, markers for viral insertions and the discovery of structural variants currently missed with short-read sequencing.

Necessary skills for this position:

• Background in bioinformatics or computational biology