Identification of distinct immune landscapes using an automated nine-color multiplex immunofluorescence staining panel and image analysis in paraffin tumor tissues.

Immune profiling is becoming a vital tool for identifying predictive and prognostic markers for translational studies. The study of the tumor microenvironment (TME) in paraffin tumor tissues such as malignant pleural mesothelioma (MPM) could yield insights to actionable targets to improve patient outcome. Here, we optimized and tested a new immune-profiling method to characterize immune cell phenotypes in paraffin tissues and explore the co-localization and spatial distribution between the immune cells within the TME and the stromal or tumor compartments. Tonsil tissues and tissue microarray (TMA) were used to optimize an automated nine-color multiplex immunofluorescence (mIF) panel to study the TME using eight antibodies: PD-L1, PD-1, CD3, CD8, Foxp3, CD68, KI67, and pancytokeratin. To explore the potential role of the cells into the TME with this mIF panel we applied this panel in twelve MPM cases to assess the multiple cell phenotypes obtained from the image analysis and well as their spatial distribution in this cohort. We successful optimized and applied an automated nine-color mIF panel to explore a small set of MPM cases. Image analysis showed a high degree of cell phenotype diversity with immunosuppression patterns in the TME of the MPM cases. Mapping the geographic cell phenotype distribution in the TME, we were able to identify two distinct, complex immune landscapes characterized by specific patterns of cellular distribution as well as cell phenotype interactions with malignant cells. Successful we showed the optimization and reproducibility of our mIF panel and their incorporation for comprehensive TME immune profiling into translational studies that could refine our ability to correlate immunologic phenotypes with specific patterns of cells distribution and distance analysis. Overall, this will improve our ability to understand the behavior of cells within the TME and predict new treatment strategies to improve patient outcome.