Lymphomas account for approximately 15-25% of all canine cancers and are the most common cancer of the blood and immune system in dogs, accounting for about 80% of this tumor type. Standard of care treatment for canine lymphoma is multiagent chemotherapy that results in median survival times of approximately one year. Despite advancements in the treatment of lymphoma in people, such as immunotherapy, little has changed in our approach to the treatment of canine lymphoma over the past two decades. Radiation therapy is known to be highly effective against canine lymphoma, but external beam radiation therapy cannot be used in most cases due to the multicentric tissue involvement and the catastrophic side effects that would occur with whole-body radiation. Radiopharmaceutical cocktails designed to specifically target tumor cells are an attractive therapy for canine lymphoma. However, an impediment of radiopharmaceutical therapies in cancer treatment is the nonuniform distribution of these therapeutic agents due to the heterogeneity of target expression by tumor cells.

The investigators propose to use a new artificial intelligence (AI) feature within the established MIRDcell software platform to optimize cocktails of radiopharmaceutical-labeled antibodies that target different markers on canine B cell lymphoma cells. The team will use lymphoma cells collected from patients and identify the density of three unique markers on the cancer cell surface. The expression pattern of these markers will be evaluated by AI and used to propose a patient-specific radiopharmaceutical cocktail to have optimal killing of tumor cells while sparing adjacent healthy cells from radiation-induced side effects. This research provides proof-of-concept for the proposed therapeutic approach and future clinical trials will be necessary to determine the efficacy of the AIgenerated patient-specific radiopharmaceutical cocktails.