Abstract 924P
Background
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.
Methods
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.
Results
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.
Conclusions
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.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
Has not received any funding.
Disclosure
All authors have declared no conflicts of interest.