Background And Objective: Quantity and the spatial relationship of specific immune cell types can provide prognostic information in bladder cancer. The objective of the study was to characterize the spatial interplay and prognostic role of different immune cell subpopulations in bladder cancer.
Methods: A total of 2463 urothelial bladder carcinomas were immunostained with 21 antibodies using BLEACH&STAIN multiplex fluorescence immunohistochemistry in a tissue microarray format and analyzed using a framework of neuronal networks for an image analysis.
Introduction: Trophoblast cell surface antigen 2 (TROP2; EpCAM2) is a transmembrane glycoprotein which is closely related to EpCAM (EpCAM; EpCAM1). Both proteins share partial overlapping functions in epithelial development and EpCAM expression but have not been comparatively analyzed together in bladder carcinomas. TROP2 constitutes the target for the antibody-drug conjugate Sacituzumab govitecan (SG; TrodelvyTM) which has been approved for treatment of metastatic urothelial carcinoma by the United States Food and Drug administration (FDA) irrespective of its TROP2 expression status.
View Article and Find Full Text PDFUnlabelled: Multiplex fluorescence IHC (mfIHC) approaches were yet either limited to six markers or limited to a small tissue size that hampers translational studies on large tissue microarray cohorts. Here we have developed a BLEACH&STAIN mfIHC method that enabled the simultaneous analysis of 15 biomarkers (PD-L1, PD-1, CTLA-4, panCK, CD68, CD163, CD11c, iNOS, CD3, CD8, CD4, FOXP3, CD20, Ki67, and CD31) in 3,098 tumor samples from 44 different carcinoma entities within one week. To facilitate automated immune checkpoint quantification on tumor and immune cells and study its spatial interplay an artificial intelligence-based framework incorporating 17 different deep-learning systems was established.
View Article and Find Full Text PDFFocal T lymphocyte aggregates commonly occur in colorectal cancer; however, their biological significance is unknown. To study focal aggregates of T lymphocytes, a deep learning-based framework for automated identification of T cell accumulations (T cell nests) was developed using CD8, PD-1, CD112R, and Ki67 multiplex fluorescence immunohistochemistry. To evaluate the clinical significance of these parameters, a cohort of 523 colorectal cancers with clinical follow-up data was analyzed.
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