Allam Mayar, Hu Thomas, Lee Jeongjin, Aldrich Jeffrey, Badve Sunil S, Gökmen-Polar Yesim, Bhave Manali, Ramalingam Suresh S, Schneider Frank, Coskun Ahmet F
Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
NPJ Precis Oncol. 2022 Sep 1;6(1):60. doi: 10.1038/s41698-022-00305-4.
The Immunoscore is a method to quantify the immune cell infiltration within cancers to predict the disease prognosis. Previous immune profiling approaches relied on limited immune markers to establish patients' tumor immunity. However, immune cells exhibit a higher-level complexity that is typically not obtained by the conventional immunohistochemistry methods. Herein, we present a spatially variant immune infiltration score, termed as SpatialVizScore, to quantify immune cells infiltration within lung tumor samples using multiplex protein imaging data. Imaging mass cytometry (IMC) was used to target 26 markers in tumors to identify stromal, immune, and cancer cell states within 26 human tissues from lung cancer patients. Unsupervised clustering methods dissected the spatial infiltration of cells in tissue using the high-dimensional analysis of 16 immune markers and other cancer and stroma enriched labels to profile alterations in the tumors' immune infiltration patterns. Spatially resolved maps of distinct tumors determined the spatial proximity and neighborhoods of immune-cancer cell pairs. These SpatialVizScore maps provided a ranking of patients' tumors consisting of immune inflamed, immune suppressed, and immune cold states, demonstrating the tumor's immune continuum assigned to three distinct infiltration score ranges. Several inflammatory and suppressive immune markers were used to establish the cell-based scoring schemes at the single-cell and pixel-level, depicting the cellular spectra in diverse lung tissues. Thus, SpatialVizScore is an emerging quantitative method to deeply study tumor immunology in cancer tissues.
免疫评分是一种量化癌症内免疫细胞浸润以预测疾病预后的方法。以往的免疫分析方法依赖于有限的免疫标志物来确定患者的肿瘤免疫情况。然而，免疫细胞表现出更高层次的复杂性，这通常是传统免疫组织化学方法无法获得的。在此，我们提出一种空间变异免疫浸润评分，称为SpatialVizScore，以利用多重蛋白质成像数据量化肺肿瘤样本中的免疫细胞浸润情况。成像质谱流式细胞术（IMC）用于靶向肿瘤中的26种标志物，以识别来自肺癌患者的26个人体组织中的基质、免疫和癌细胞状态。无监督聚类方法利用16种免疫标志物以及其他癌症和基质富集标记物的高维分析来剖析组织中细胞的空间浸润情况，以描绘肿瘤免疫浸润模式的变化。不同肿瘤的空间分辨图谱确定了免疫-癌细胞对的空间邻近性和邻域。这些SpatialVizScore图谱提供了患者肿瘤的排名，包括免疫炎症、免疫抑制和免疫冷状态，展示了分配给三个不同浸润评分范围的肿瘤免疫连续体。使用了几种炎症和抑制性免疫标志物来建立单细胞和像素水平基于细胞的评分方案，描绘不同肺组织中的细胞光谱。因此，SpatialVizScore是一种新兴的定量方法，用于深入研究癌症组织中的肿瘤免疫学。
