一种用于克服非小细胞肺癌治疗中抗原异质性的多靶向 CAR-T 细胞平台
A Multi-Targeting Chimeric Antigen Receptor-T Cell Platform to Overcome Antigen Heterogeneity in the Treatment of Non-Small Cell Lung Cancer.
这些发现支持采用多靶点CAR-T 策略来应对NSCLC及可能其他实体瘤中的抗原异质性。
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
英文原题:Immune cellular patterns of distribution affect outcomes of patients with non-small cell lung cancer.
Immune cellular patterns of distribution affect outcomes of patients with non-small cell lung cancer.
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研究非小细胞肺癌中的细胞地理分布对于理解细胞群体在该类肿瘤中的作用至关重要。在本研究中,我们利用置于五个多重免疫荧光panel中的23个标志物来表征免疫细胞群体的空间细胞分布,及其与临床病理变量和结局的关联。我们的结果展示了两种细胞分布模式——一种主要与免疫保护性细胞相关的未混合模式,以及一种主要与免疫抑制性细胞相关的混合模式。距离分析显示,表达免疫检查点的T细胞比其他细胞更靠近恶性细胞。将细胞分布模式与细胞距离相结合,我们可以识别出与炎症型和非炎症型肿瘤相关的四组。在单变量和多变量分析中,细胞分布模式和距离均与生存相关。空间分布是更好地理解肿瘤微环境、预测结局的工具,并可能有助于选择治疗干预措施。
Studying the cellular geographic distribution in non-small cell lung cancer is essential to understand the roles of cell populations in this type of tumor. In this study, we characterize the spatial cellular distribution of immune cell populations using 23 makers placed in five multiplex immunofluorescence panels and their associations with clinicopathologic variables and outcomes.
Our results demonstrate two cellular distribution patterns-an unmixed pattern mostly related to immunoprotective cells and a mixed pattern mostly related to immunosuppressive cells. Distance analysis shows that T-cells expressing immune checkpoints are closer to malignant cells than other cells.
Combining the cellular distribution patterns with cellular distances, we can identify four groups related to inflamed and not-inflamed tumors. Cellular distribution patterns and distance are associated with survival in univariate and multivariable analyses. Spatial distribution is a tool to better understand the tumor microenvironment, predict outcomes, and may can help select therapeutic interventions.
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