Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, Florida, United States of America.
Department of Health Informatics, Moffitt Cancer Center, Tampa, Florida, United States of America.
PLoS Comput Biol. 2022 Mar 2;18(3):e1009900. doi: 10.1371/journal.pcbi.1009900. eCollection 2022 Mar.
New technologies, such as multiplex immunofluorescence microscopy (mIF), are being developed and used for the assessment and visualization of the tumor immune microenvironment (TIME). These assays produce not only an estimate of the abundance of immune cells in the TIME, but also their spatial locations. However, there are currently few approaches to analyze the spatial context of the TIME. Therefore, we have developed a framework for the spatial analysis of the TIME using Ripley's K, coupled with a permutation-based framework to estimate and measure the departure from complete spatial randomness (CSR) as a measure of the interactions between immune cells. This approach was then applied to epithelial ovarian cancer (EOC) using mIF collected on intra-tumoral regions of interest (ROIs) and tissue microarrays (TMAs) from 160 high-grade serous ovarian carcinoma patients in the African American Cancer Epidemiology Study (AACES) (94 subjects on TMAs resulting in 263 tissue cores; 93 subjects with 260 ROIs; 27 subjects with both TMA and ROI data). Cox proportional hazard models were constructed to determine the association of abundance and spatial clustering of tumor-infiltrating lymphocytes (CD3+), cytotoxic T-cells (CD8+CD3+), and regulatory T-cells (CD3+FoxP3+) with overall survival. Analysis was done on TMA and ROIs, treating the TMA data as validation of the findings from the ROIs. We found that EOC patients with high abundance and low spatial clustering of tumor-infiltrating lymphocytes and T-cell subsets in their tumors had the best overall survival. Additionally, patients with EOC tumors displaying high co-occurrence of cytotoxic T-cells and regulatory T-cells had the best overall survival. Grouping women with ovarian cancer based on both cell abundance and spatial contexture showed better discrimination for survival than grouping ovarian cancer cases only by cell abundance. These findings underscore the prognostic importance of evaluating not only immune cell abundance but also the spatial contexture of the immune cells in the TIME. In conclusion, the application of this spatial analysis framework to the study of the TIME could lead to the identification of immune content and spatial architecture that could aid in the determination of patients that are likely to respond to immunotherapies.
新技术，如多重免疫荧光显微镜（mIF），正在被开发和应用于评估和可视化肿瘤免疫微环境（TIME）。这些检测不仅可以估计 TIME 中免疫细胞的丰度，还可以确定它们的空间位置。然而，目前很少有方法来分析 TIME 的空间背景。因此，我们开发了一种使用 Ripley's K 进行 TIME 空间分析的框架，并结合基于置换的框架来估计和测量偏离完全空间随机性（CSR）的程度，作为免疫细胞之间相互作用的度量。然后，我们将该方法应用于非洲裔美国癌症流行病学研究（AACES）中 160 例高级别浆液性卵巢癌患者的肿瘤内感兴趣区域（ROI）和组织微阵列（TMA）上收集的上皮性卵巢癌（EOC）mIF 数据（94 例 TMA 患者产生 263 个组织芯；93 例 ROI 患者；27 例 TMA 和 ROI 数据患者）。构建 Cox 比例风险模型以确定肿瘤浸润淋巴细胞（CD3+）、细胞毒性 T 细胞（CD8+CD3+）和调节性 T 细胞（CD3+FoxP3+）的丰度和空间聚类与总生存的关联。在 TMA 和 ROI 上进行分析，将 TMA 数据视为 ROI 结果的验证。我们发现，肿瘤中浸润淋巴细胞和 T 细胞亚群丰度高且空间聚类低的 EOC 患者总体生存情况最好。此外，肿瘤中细胞毒性 T 细胞和调节性 T 细胞高共现的 EOC 患者总体生存情况最好。根据细胞丰度和空间结构对卵巢癌患者进行分组，比仅根据细胞丰度对卵巢癌病例进行分组，对生存的区分能力更好。这些发现强调了评估肿瘤免疫微环境中免疫细胞丰度和空间结构的预后重要性。总之，将这种空间分析框架应用于 TIME 的研究可能会识别出有助于确定可能对免疫治疗有反应的患者的免疫内容和空间结构。
