Multiplex fluorescence immunohistochemistry (mfIHC) approaches were yet either limited to 6 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, CD31) in 3098 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. Unsupervised clustering showed that the three PD-L1 phenotypes (PD-L1+tumor and immune cells, PD-L1+immune cells, PD-L1 negative) were either inflamed or non-inflamed. In the inflamed PD-L1+patients, spatial analysis revealed that an elevated intratumoral M2-macrophages as well as CD11c+dendritic cell infiltration (p<0.001 each) was associated with a high CD3+CD4±CD8±FOXP3±T-cell exclusion and a high PD-1 expression on T-cells (p<0.001 each). In breast cancer, the PD-L1 fluorescence intensity on tumor cells showed a significantly higher predictive performance for overall survival (AUC: 0.72, p<0.001) compared to the commonly used percentage of PD-L1+ tumor cells (AUC: 0.54). In conclusion, our deep learning-based BLEACH&STAIN framework facilitates rapid and comprehensive assessment of more than 60 spatially orchestrated immune cell subpopulations and its prognostic relevance. Implications: The development of an easy-to-use high-throughput 15+1 multiplex fluorescence approach facilitates the in-depth understanding of the immune tumor microenvironment and enables to study the prognostic relevance of more than 130 immune cell subpopulations.
