Tumor immune cell clustering and its association with survival in African American women with ovarian cancer.
Tumor immune cell clustering and its association with survival in African American women with ovarian cancer.
复制标题
DOI：
10.1371/journal.pcbi.1009900
复制
发表时间：
2022-03
影响因子：
4.3
通讯作者：
Fridley BL
中科院分区：
文献类型：
--
作者：
Wilson C;Soupir AC;Thapa R;Creed J;Nguyen J;Segura CM;Gerke T;Schildkraut JM;Peres LC;Fridley BL
多重免疫荧光显微镜 (mIF) 等新技术正在开发中，用于肿瘤免疫微环境 (TIME) 的评估和可视化。这些测定不仅可以估计当时免疫细胞的丰度，还可以估计它们的空间位置。然而，目前分析 TIME 空间背景的方法很少。因此，我们开发了一个使用 Ripley’s K 进行 TIME 空间分析的框架，并结合基于排列的框架来估计和测量与完全空间随机性 (CSR) 的偏离，作为免疫细胞之间相互作用的度量。然后，使用从非裔美国人癌症流行病学研究 (AACES) 中的 160 名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 mIF，将该方法应用于上皮性卵巢癌 (EOC)（94 名受试者使用 TMA 产生 263 个组织核心；93 名受试者有 260 个 ROI；27 名受试者同时拥有 TMA 和 ROI 数据）。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+) 的丰度和空间聚集与总体生存的关联。对 TMA 和 ROI 进行分析，将 TMA 数据视为对 ROI 结果的验证。我们发现，肿瘤中肿瘤浸润淋巴细胞和 T 细胞亚群丰度高、空间聚集度低的 EOC 患者总体生存率最高。此外，细胞毒性 T 细胞和调节性 T 细胞高度共存的 EOC 肿瘤患者的总体生存率最高。根据细胞丰度和空间背景对患有卵巢癌的女性进行分组，比仅根据细胞丰度对卵巢癌病例进行分组显示出更好的生存区分度。这些发现强调了不仅评估免疫细胞丰度而且评估免疫细胞在 TIME 中的空间背景的预后重要性。总之，将该空间分析框架应用于 TIME 研究可能会导致免疫内容和空间结构的识别，从而有助于确定可能对免疫疗法产生反应的患者。多重免疫荧光显微镜等新技术正在开发中，用于肿瘤免疫微环境 (TIME) 的评估和可视化。这些测定不仅可以估计当时免疫细胞的丰度，还可以估计它们的空间位置。然而，目前分析 TIME 空间背景的方法很少。因此，我们开发了一个 TIME 空间分析框架，并将该方法应用于非裔美国人癌症流行病学研究中从高级别浆液性卵巢癌患者收集的 T 细胞分析。我们发现，肿瘤中肿瘤浸润淋巴细胞和 T 细胞亚群丰度高、空间聚集度低的患者总体生存率最高。此外，细胞毒性 T 细胞和调节性 T 细胞高度共存的肿瘤患者的生存率最高。这些发现强调了不仅评估免疫细胞丰度而且评估卵巢 TIME 中免疫细胞的空间背景的预后重要性。使用我们的时间和免疫细胞聚类空间分析框架可能适用于其他癌症，并提供一种识别生物标志物以预测患者结果的新方法。
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. New technologies, such as multiplex immunofluorescence microscopy, 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. Thus, we have developed a framework for the spatial analysis of the TIME and applied this method to the analysis of T-cells collected from patients with high-grade serous ovarian carcinoma in the African American Cancer Epidemiology Study. We found that 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, best survival was observed for patients with tumors displaying high co-occurrence of cytotoxic T-cells and regulatory T-cells. These findings underscore the prognostic importance of evaluating not only immune cell abundance but also the spatial contexture of the immune cells in the ovarian TIME. The use of our framework for spatial analysis of the TIME and immune cell clustering may be applicable in other cancers and provide a novel approach to identification of biomarkers for predicting patient outcomes.
登录
查看更多内容
DOI：
10.1038/nrc3239
发表时间：
2012-03-22
期刊：
Nature reviews. Cancer
影响因子：
--
作者：
Pardoll DM
通讯作者：
Pardoll DM
影响因子：
1.6
作者：
Jawhar NM
通讯作者：
Jawhar NM
影响因子：
1.8
作者：
Lausen, B;Schumacher, M
通讯作者：
Schumacher, M
影响因子：
3.4
作者：
Kiskowski, Maria A.;Hancock, John F.;Kenworthy, Anne K.
通讯作者：
Kenworthy, Anne K.
影响因子：
1.9
作者：
LAUSEN, B;SCHUMACHER, M
通讯作者：
SCHUMACHER, M