Balcioglu Hayri E, Wijers Rebecca, Smid Marcel, Hammerl Dora, Trapman-Jansen Anita M, Oostvogels Astrid, Timmermans Mieke, Martens John W M, Debets Reno
Laboratory of Tumor Immunology, Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
Laboratory of Translational Cancer Genomics, Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
Npj Imaging. 2024 Jul 25;2(1):21. doi: 10.1038/s44303-024-00022-6.
Spatial distribution of intra-tumoral immune cell populations is considered a critical determinant of tumor evolution and response to therapy. The accurate and systemic search for contexture-based predictors would be accelerated by methods that allow interactive visualization and interrogation of tumor micro-environments (TME), independent of image acquisition platforms. To this end, we have developed the TME-Analyzer, a new image analysis tool, which we have benchmarked against 2 software tools regarding densities and networks of immune effector cells using multiplexed immune-fluorescent images of triple negative breast cancer (TNBC). With the TME-Analyzer we have identified a 10-parameter classifier, predominantly featuring cellular distances, that significantly predicted overall survival, and which was validated using multiplexed ion beam time of flight images from an independent cohort. In conclusion, the TME-Analyzer enabled accurate interactive analysis of the spatial immune phenotype from different imaging platforms as well as enhanced utility and aided the discovery of contextual predictors towards the survival of TNBC patients.
肿瘤内免疫细胞群体的空间分布被认为是肿瘤演变和对治疗反应的关键决定因素。能够独立于图像采集平台对肿瘤微环境（TME）进行交互式可视化和询问的方法，将加速对基于组织结构的预测指标进行准确和系统的搜索。为此，我们开发了TME分析器，这是一种新的图像分析工具，我们使用三阴性乳腺癌（TNBC）的多重免疫荧光图像，针对另外2种软件工具，对免疫效应细胞的密度和网络进行了基准测试。借助TME分析器，我们确定了一个主要以细胞距离为特征的10参数分类器，该分类器能显著预测总生存期，并使用来自一个独立队列的多重离子束飞行时间图像进行了验证。总之，TME分析器能够对来自不同成像平台的空间免疫表型进行准确的交互式分析，增强了实用性，并有助于发现TNBC患者生存的背景预测指标。
