Institut de Recherche Expérimentale et Clinique (IREC), Pôle MIRO, Université Catholique de Louvain (UCLouvain).
IREC Imaging Platform, Université Catholique de Louvain (UCLouvain).
J Vis Exp. 2023 Jun 2(196). doi: 10.3791/65220.
The tumor microenvironment (TME) is composed of a plethora of different cell types, such as cytotoxic immune cells and immunomodulatory cells. Depending on its composition and the interactions between cancer cells and peri-tumoral cells, the TME may affect cancer progression. The characterization of tumors and their complex microenvironment could improve the understanding of cancer diseases and may help scientists and clinicians to discover new biomarkers. We recently developed several multiplex immunofluorescence (mIF) panels based on tyramide signal amplification (TSA) for the characterization of the TME in colorectal cancer, head and neck squamous cell carcinoma, melanoma, and lung cancer. Once the staining and scanning of the corresponding panels are completed, the samples are analyzed on an image analysis software. The spatial position and the staining of each cell are then exported from this quantification software into R. We developed R scripts that allow us not only to analyze the density of each cell type in several tumor compartments (e.g. the center of the tumor, the margin of the tumor, and the stroma) but also to perform distance-based analyses between different cell types. This particular workflow adds a spatial dimension to the classical density analysis already routinely performed for several markers. mIF analysis could allow scientists to have a better understanding of the complex interaction between cancer cells and the TME and to discover new predictive biomarkers of response to treatments, such as immune checkpoint inhibitors, and targeted therapies.
肿瘤微环境（TME）由多种不同的细胞类型组成，如细胞毒性免疫细胞和免疫调节细胞。根据其组成和癌细胞与肿瘤周围细胞之间的相互作用，TME 可能会影响癌症的进展。对肿瘤及其复杂微环境的特征分析可以提高对癌症疾病的认识，并有助于科学家和临床医生发现新的生物标志物。我们最近开发了几种基于酪胺信号放大（TSA）的多重免疫荧光（mIF）面板，用于结直肠癌、头颈部鳞状细胞癌、黑色素瘤和肺癌的 TME 特征分析。完成相应面板的染色和扫描后，在图像分析软件上分析样本。然后从定量软件中将每个细胞的空间位置和染色输出到 R 中。我们开发了 R 脚本，不仅可以分析几个肿瘤区室（如肿瘤中心、肿瘤边缘和基质）中每种细胞类型的密度，还可以对不同细胞类型之间进行基于距离的分析。这种特定的工作流程为已经常规用于多个标记物的密度分析添加了空间维度。mIF 分析可以帮助科学家更好地理解癌细胞与 TME 之间的复杂相互作用，并发现新的治疗反应预测生物标志物，如免疫检查点抑制剂和靶向治疗。
