Lymphoma is a prevalent type of cancer that arises from the transformation of B an T lymphocytes through the accumulation of somatic mutations that affect fundamental cellular functions, such as growth and division. Although most somatic mutations occur in the non-coding regions of the genome, comparatively few studies have focused on identifying non-coding driver mutation, due perhaps to the associated complexity and cost relative to protein-coding regions. However, non-coding elements play a crucial role in gene regulation and cellular functions and alterations in these regions have the potential to contribute to tumor formation.
Identifying non-coding driver mutations poses a significant challenge, owing to the extensive size of the non-coding genome, the presence of repetitive DNA, and the limited understanding of this region. Moreover, the identification of regulatory elements within non-coding regions remains an ongoing endeavor. Therefore, our research group proposed the use of evolutionary constraint as an indicator of regions that have been conserved throughout evolution—and therefore have functional potential—in which a disruption could give rise to tumorigenesis. This hypothesis led to the publication of a research paper published in 2020, which asserted that non-coding constraint mutations may play a role in the development of glioblastoma, an aggressive form of brain cancer. The outcomes of this study provided a foundation for various cancer projects, including the present lymphoma project.
The aim of the lymphoma project is to identify and validate novel driver variants present in non-coding regions that could potentially contribute to lymphomagenesis. On top of this, our group is interested in the use of the dog as a model for lymphoma research given its clinical and genetic similarity to humans. Therefore, this project will also investigate the shared and species-exclusive driver variants in canine and human lymphoma. Whole-genome sequencing data will be utilized to pinpoint candidate driver mutations, followed by conducting expression analysis of these variants using RNAseq data.