Osteosarcoma, predominantly affecting children and adolescents, is a highly aggressive bone cancer with a 5-year survival rate of 65% to 70%. The spatial dynamics between tumor-associated macrophage (TAM) and other cellular subtypes, including T cells, osteoblasts, and osteoclasts, are critical for understanding the complexities of the osteosarcoma tumor microenvironment (TME) and can provide insights into potential immunotherapeutic strategies. Our study employs a pioneering approach that combines deep learning-based digital image analysis with multiplex fluorescence immunohistochemistry to accurately implement cell detection, segmentation, and fluorescence intensity measurements for the in-depth study of the TME. We introduce a novel algorithm for TAM/osteoclast differentiation, crucial for the accurate characterization of cellular composition. Our findings reveal distinct heterogeneity in cell composition and spatial orchestration between PD-1 (-/+) and PD-L1 (-/+) patients, highlighting the role of T-cell functionality in this context. Furthermore, our analysis demonstrates the efficacy of nivolumab in suppressing tumor growth and enhancing lymphocyte infiltration without altering the M1/M2-TAM ratio. This study provides critical insights into the spatial orchestration of cellular subtypes within the PD-1/PD-L1 defined osteosarcoma TME. By leveraging advanced multiplex fluorescence immunohistochemistry and artificial intelligence, we underscore the critical role of TAMs and T-cell interactions, proposing new therapeutic avenues focusing on TAM repolarization and targeted immunotherapies, thus underscoring the study's potential impact on improving osteosarcoma treatment.
Keywords: PD-1; PD-L1; artificial intelligence; immunotherapy; multiplex fluorescence immunohistochemistry.
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