Developing Naturally Occurring Cancers in Dogs as Translational Models
While laboratory mouse models are widely used for preclinical testing in cancer research, their clinical translation is limited, and the success rate of of effective drug development is extremely low. Dogs are the most cancer-susceptible species, except for humans, yet the question of why cancer is so common in this species remains unanswered. Importantly, the translational potential of canine cancers has yet to be fully elucidated. We study cancers in dogs to benefit both canine and human patients.
AI and Machine Learning for Diagnostic and Clinical Applications in Veterinary and Comparative Medicine
We focus on developing novel diagnostic and therapeutic applications for human and animal diseases using AI and machine learning.
Our current projects involves utilizing convolutional neural networks on 1) cytological images and 2) histological images obtained from whole-slide scanning of naturally occurring cancers in companion dogs. In addition to veterinary applications, we are also developing comparative oncology approaches to applying for human cancer applications. 3) We also utilize electronic medical data from animal patients to develop a novel AI-driven analytic platform for a veterinary digital health care system.
Genetic Mutations and Tumor Immunity
We determine the fundamental mechanisms of oncogenesis and tumor progression. Our work involves delineating the functional consequences of genetic mutations that create a tumor-specific niche and establish the immune landscape in the tissue microenvironment. Our research particularly emphasizes aggressive bone and soft tissue sarcomas such as osteosarcoma and angiosarcoma.
Technological Advances and Novel Tools for Comparative Cancer Research
We establish novel modeling systems for comparative oncology research using iPSC modeling, genetic screen, molecular imaging, biosensor development, and early detection method. Specifically, our efforts include genome-wide CRISPR screen, proximity labeling, and the identification of cell-free genetic molecules.
Update: October 1, 2026
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