Roche Pharma Research and Early Development (pRED), Penzberg, Germany, and Basel, Switzerland.
J Pathol. 2013 Mar;229(4):569-78. doi: 10.1002/path.4150.
Evaluation of specific lymphocyte subsets is important in understanding the microenvironment in cancer and holds promise as a prognostic parameter in invasive breast cancer. To address this, we used digital image analysis to integrate cell abundance, distance metrics, neighbourhood relationships and sample heterogeneity into comprehensive assessment of immune infiltrates. Lymphocyte and macrophage subpopulations were detected by chromogenic duplex immunohistochemistry for CD3/perforin and CD68/CD163 in samples of invasive breast cancer. The analysis workflow combined commercial and open-source software modules. We confirmed the accuracy of automated detection of cells with lymphoid morphology [concordance correlation coefficient (CCC), 0.92 for CD3(+) -T lymphocytes], whereas variable morphology limited automated classification of macrophages as distinct cellular objects (CCC, 0.43 for object-based detection; 0.79 for pixel-based area analysis). Using a supervised learning algorithm that clustered image areas according to lymphocyte abundance, grouping behaviour and distance to tumour cells, we identified recurrent infiltration patterns reflecting different grades of direct interaction between tumour and immune effector cells. The approach provided comprehensive visual and statistical assessment of the inflammatory tumour microenvironment and allowed quantitative estimation of heterogeneous immune cell distribution. Cases with dense lymphocytic infiltrates (8/33) contained up to 65% of areas in which observed distances between tumour and immune cells suggested a low chance of direct contact, indicating the presence of regions where tumour cells might be protected from immune attack. In contrast, cases with moderate (11/33) or low (14/33) lymphocyte density occasionally comprised areas of focally intense interaction, likely not to be captured by conventional scores. Our approach improves the conventional evaluation of immune cell density scores by translating objective distance metrics into reproducible, largely observer-independent interaction patterns.
评估特定的淋巴细胞亚群对于理解癌症微环境非常重要，并有望成为浸润性乳腺癌的预后参数。为了解决这个问题，我们使用数字图像分析将细胞丰度、距离度量、邻域关系和样本异质性整合到免疫浸润的综合评估中。通过对浸润性乳腺癌样本进行 CD3/穿孔素和 CD68/CD163 的显色双免疫组化，检测淋巴细胞和巨噬细胞亚群。分析工作流程结合了商业和开源软件模块。我们验证了具有淋巴样形态的细胞的自动检测的准确性[一致性相关系数（CCC），对于 CD3(+) -T 淋巴细胞为 0.92]，而形态可变则限制了巨噬细胞作为不同细胞对象的自动分类（CCC，对于基于对象的检测为 0.43；对于基于像素的面积分析为 0.79）。使用根据淋巴细胞丰度、分组行为和与肿瘤细胞的距离对图像区域进行聚类的监督学习算法，我们确定了反映肿瘤和免疫效应细胞之间直接相互作用不同程度的反复浸润模式。该方法提供了炎症性肿瘤微环境的全面视觉和统计评估，并允许对异质免疫细胞分布进行定量估计。密集淋巴细胞浸润的病例（8/33）中，多达 65%的区域观察到肿瘤和免疫细胞之间的距离表明直接接触的可能性较低，这表明存在肿瘤细胞可能免受免疫攻击的区域。相比之下，中等（11/33）或低（14/33）淋巴细胞密度的病例偶尔包含局灶性强烈相互作用的区域，这可能无法通过传统评分来捕捉。我们的方法通过将客观距离度量转化为可重复的、在很大程度上观察者独立的相互作用模式，改进了免疫细胞密度评分的传统评估。
