Iris Casselman

Cancer accounts for approximately 27% of deaths in dogs, with canine lymphoma (CL) representing around 15% of all canine cancers. CL is a heterogeneous group of lymphoid malignancies with distinct biological characteristics, clinical behaviour and responses to treatment. Despite these differences, diagnosing and accurately subtyping CL remains challenging. Current diagnostic workflows often require multiple complementary techniques, involve subjective methods, and can be time-consuming.

My research aims to investigate the genome-wide landscape of CL while developing a rapid DNA based diagnostic test using minimally invasive fine-needle aspirate (FNA) samples. This approach combines straightforward sampling with third-generation sequencing and artificial intelligence-based classification to enable faster, more cost-effective and objective molecular diagnosis.

Our workflow begins with DNA extraction from an FNA sample, followed by third-generation sequencing to generate molecular profiles. These data are processed using bioinformatics pipelines and integrated into classification algorithms that 1/ distinguish lymphoma from reactive lymphoid conditions and 2/ accurately assign lymphoma subtype.

The research is driven by three interconnected questions:

• Can molecular alterations distinguish lymphoma from reactive lymphoid tissue?

• Can molecular signatures discriminate between 3 clinically relevant subtypes: high-grade B cell lymphoma, low-grade T cell lymphoma (TCL) and high-grade TCL?

• Are there molecular patterns associated with survival?

Together, this work moves from detecting lymphoma, to defining its molecular subtype, to understanding its prognostic biology. Ultimately, it aims to deliver faster, more precise molecular diagnostics while providing new insight into the mechanisms driving canine lymphoma.
 

• Lab address: Heidestraat 19, 9820 Merelbeke-Melle (Belgium)
• CRIG Veterinary Oncology Network (VON) 
• Iris Casselman is interested to receive invitations for presentations or talks