Department of Spine Surgery, The Affiliated Ganzhou Hospital of Nanchang University, Ganzhou, China.
Department of Pathology, The Affiliated Ganzhou Hospital of Nanchang University, Ganzhou, China.
Lab Invest. 2024 Nov;104(11):102143. doi: 10.1016/j.labinv.2024.102143. Epub 2024 Sep 23.
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.
骨肉瘤主要影响儿童和青少年,是一种高度侵袭性的骨癌,5 年生存率为 65%至 70%。肿瘤相关巨噬细胞(TAM)与其他细胞亚型(包括 T 细胞、成骨细胞和破骨细胞)之间的空间动态对于理解骨肉瘤肿瘤微环境(TME)的复杂性至关重要,并为潜在的免疫治疗策略提供了思路。我们的研究采用了一种开创性的方法,将基于深度学习的数字图像分析与多重荧光免疫组化相结合,准确地实现了细胞检测、分割和荧光强度测量,以深入研究 TME。我们引入了一种新的 TAM/破骨细胞分化算法,这对于准确描述细胞组成至关重要。我们的研究结果揭示了 PD-1(+/−)和 PD-L1(+/−)患者之间细胞组成和空间协调的明显异质性,突出了 T 细胞功能在此背景下的作用。此外,我们的分析表明,nivolumab 抑制肿瘤生长和增强淋巴细胞浸润的疗效,而不改变 M1/M2-TAM 比值。这项研究提供了骨肉瘤 PD-1/PD-L1 定义的 TME 中细胞亚群空间协调的关键见解。通过利用先进的多重荧光免疫组化和人工智能,我们强调了 TAMs 和 T 细胞相互作用的关键作用,提出了新的治疗途径,侧重于 TAM 再极化和靶向免疫治疗,从而强调了该研究在改善骨肉瘤治疗方面的潜在影响。