924P BLEACH&STAIN, a novel multiplex fluorescence immunohistochemistry framework that facilitates a fast high-throughput analysis of >15 biomarkers in more than 3000 human carcinomas

Abstract

Multiplex fluorescence immunohistochemistry (mfIHC) approaches were yet either limited to 6 markers or limited to a small (1.5cmx1.5cm) tissue size that hampers translational studies on large tissue microarray (TMA) cohorts. To assess more markers in a large patient cohort, we have developed a BLEACH&STAIN mfIHC approach that enables the analysis of ≥15 biomarkers in 3098 tumor samples from 44 different carcinoma entities within one week and without costly instrumentalization. An artificial intelligence-based framework – incorporating three different deep learning systems – for automated marker quantification was used to interoperate the BLEACH&STAIN data. This approach was used to study the relationship between PD-L1 expression on multiple different cell types and the relationship with various leucocyte subtypes (PD-L1, PD-1, CTLA-4, panCK, CD68, CD163, CD11c, iNOS, CD3, CD8, CD4, FOXP3, CD20, Ki67, CD31). Comparing the automated and deep learning-based BLEACH&STAIN PD-L1 analysis framework with conventional brightfield PD-L1 data revealed a high concordance in tumor cells (p<0.0001) as well as immune cells (p<0.0001) and an accuracy of our approach ranging from 90% to 95.2%. Unsupervised clustering showed that a major proportion of the three PD-L1 phenotypes (i.e., PD-L1+ tumor and immune cells [G1], PD-L1+ immune cells [G2], PD-L1 negative [G3]) were either inflamed (G1.1, G2.1, G3.1) or non-inflamed (G1.2, G2.2, G3.2) and showed distinct spatial orchestration patterns. BLEACH&STAIN mfIHC in combination with a deep learning-based framework for automated PD-L1 assessment on tumor and immune cells enabled a rapid and comprehensive assessment of 15 biomarkers across more than 3000 tumor entities that is quick and easy to establish in all laboratories. In breast cancer, the PD-L1 relative expression on tumor cells showed a significantly higher predictive performance for overall survival compared to the commonly used PD-L1 tumor proportion score.