National Center of Pathology, Vilnius University Hospital Santaros Clinics, Vilnius, Lithuania; Department of Pathology, Forensic Medicine and Pharmacology, Institute of Biomedical Sciences, Vilnius University, Vilnius, Lithuania.
National Center of Pathology, Vilnius University Hospital Santaros Clinics, Vilnius, Lithuania; Department of Pathology, Forensic Medicine and Pharmacology, Institute of Biomedical Sciences, Vilnius University, Vilnius, Lithuania.
Am J Pathol. 2020 Jun;190(6):1309-1322. doi: 10.1016/j.ajpath.2020.01.018. Epub 2020 Mar 17.
The distribution of tumor-infiltrating lymphocytes (TILs) within the tumor microenvironment provides strong prognostic value, which is increasingly important with the arrival of new immunotherapy modalities. Both visual and image analysis-based assays are developed to assess the immune contexture of the tumors. We propose an automated method based on grid subsampling of microscopy image analysis data to extract the tumor-stroma interface zone (IZ) of controlled width. The IZ is a ranking of tissue areas by their distance to the tumor edge, which is determined by a set of explicit rules. TIL density profiles across the IZ are used to compute a set of novel immunogradient indicators that reflect TIL gradient towards the tumor. We applied this method on CD8 immunohistochemistry images of surgically excised hormone receptor-positive breast and colorectal cancers to predict overall patient survival. In both cohorts, the immunogradient indicators enabled strong and independent prognostic stratification, outperforming clinical and pathologic variables. Patients with breast cancer with low immunogradient levels had a prominent decrease in survival probability 5 years after surgery. Our study provides proof of concept that data-driven, automated, operator-independent IZ sampling enables spatial immune response measurement in the tumor-host interaction frontline for prediction of disease outcomes.
肿瘤浸润淋巴细胞 (TILs) 在肿瘤微环境中的分布提供了强大的预后价值，随着新的免疫治疗模式的出现，这一点变得越来越重要。目前已经开发出基于视觉和图像分析的检测方法来评估肿瘤的免疫结构。我们提出了一种基于显微镜图像分析数据网格抽样的自动化方法，以提取具有可控宽度的肿瘤-基质界面区 (IZ)。IZ 是根据距离肿瘤边缘的组织区域的距离对组织区域进行排序的，这是通过一组明确的规则来确定的。在 IZ 上，TIL 密度分布用于计算一组新的免疫梯度指标，这些指标反映了 TIL 向肿瘤的梯度。我们将该方法应用于手术切除的激素受体阳性乳腺癌和结直肠癌的 CD8 免疫组化图像，以预测患者的总体生存情况。在两个队列中，免疫梯度指标均能够进行强有力的独立预后分层，优于临床和病理变量。手术后 5 年，乳腺癌患者的免疫梯度水平较低，其生存率显著下降。我们的研究提供了一个概念验证，即数据驱动、自动化、无需操作人员干预的 IZ 采样能够在肿瘤-宿主相互作用的前沿进行空间免疫反应测量，从而预测疾病结局。
