A novel [89Zr]-anti-PD-1-PET-CT to assess response to PD-1/PD-L1 blockade in lung cancer

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Abstract

Background: Harnessing the anti-tumor immune system response by targeting the program cell death protein (PD-1) and program cell death ligand protein (PD-L1) axis has been a major breakthrough in non-small cell lung cancer (NSCLC) therapy. Nonetheless, conventional imaging tools cannot accurately assess response in immunotherapy-treated patients. Using a lung cancer syngeneic mouse model responder to immunotherapy, we aimed to demonstrate that [89Zr]-anti-PD-1 immuno-PET is a safe and feasible imaging modality to assess the response to PD-1/PD-L1 blockade in NSCLC. Materials and methods: A syngeneic mouse model responder to anti-PD-1 therapy was used. Tumor growth and response to PD-1 blockade were monitored by conventional 2-deoxy-2-[18F]fluoro-D-glucose ([18F]-FDG) PET scans. Additionally, tumor lymphocyte infiltration was analyzed by the use of an [89Zr]-labeled anti-PD-1 antibody and measured as 89Zr tumor uptake. Results: Conventional [18F]-FDG-PET scans failed to detect the antitumor activity exerted by anti-PD-1 therapy. However, [89Zr]-anti-PD-1 uptake was substantially higher in mice that responded to PD-1 blockade. The analysis of tumor-infiltrating immune cell populations and interleukins demonstrated an increased anti-tumor effect elicited by activation of effector immune cells in PD-1-responder mice. Interestingly, a positive correlation between [89Zr]-anti-PD-1 uptake and the proportion of tumor-infiltrating lymphocytes (TILs) was found (Cor = 0.8; p = 0.001). Conclusion: Our data may support the clinical implementation of immuno-PET as a promising novel imaging tool to predict and assess the response of PD-1/PD-L1 inhibitors in patients with NSCLC.

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Puyalto, A., Rodríguez-Remírez, M., López, I., Iribarren, F., Simón, J. A., Ecay, M., … Gil-Bazo, I. (2023). A novel [89Zr]-anti-PD-1-PET-CT to assess response to PD-1/PD-L1 blockade in lung cancer. Frontiers in Immunology, 14. https://doi.org/10.3389/fimmu.2023.1272570

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