Discovery and functional characterization of canine PD-L1-targeted antibodies for evaluating antitumor efficacy in a canine osteosarcoma xenograft model
Year: 2025
Type: article
Abstract: Targeting the programmed cell death protein 1 (PD-1)/programmed death ligand 1 (PD-L1) pathway is promising in treating cancer in humans and offers potential for veterinary applications. However, no PD-L1 antibodies have been approved specifically for treating canine cancer. We aimed to develop PD-L1-specific antibodies using phage display technology for treating canine cancer. A synthetic antibody library was screened, and 18 high-affinity single-chain variable fragment clones were subsequently converted to the IgG format for enhancing binding affinity and functional stability. The clone #15 exhibited the highest binding affinity and most pronounced antitumor effects. The PD-1/PD-L1 interaction was inhibited by antibody #15. The binding and thermal stabilities of the antibodies were validated by flow cytometry and thermal stability assays, respectively. In NOG mice xenografted with canine osteosarcoma cells and treated with canine peripheral blood mononuclear cells and antibody #15, the tumor size and weight were reduced. Antibody #15 significantly increased apoptosis of tumor cells and lymphocyte populations. Therefore, anti-PD-L1 antibodies, particularly antibody #15, have substantial potential as novel immunotherapeutic agents against canine osteosarcoma. This study represents a significant advancement in veterinary oncology, with the potential of improving treatment outcomes for canine cancers and providing insights into similar strategies in human cancer therapy.
Source: Scientific Reports
Authors: Min-young Song, Jaewon Cho, Hyosung Park, Yujeong Song, Keon Kim, Jae-Hee Ahn, Chang-Min Lee, Dae Hee Kim, Hyun-Jeong Ko
Institutions: Kangwon National University, Chonnam National University
Language: English
Topic: Veterinary Oncology Research
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,690
Award: G1269908845
OpenAlex ID: W4408122802
