Novel immunotherapy has revolutionized the landscape of cancer therapy. Growing evidence has revealed the importance of tumor microenvironment (TME) and how it may impact the response to immunotherapy. Emerging data has suggested immune biomarkers based on co-expression patterns and spatial distribution will improve predictive performance to the efficacy of immunotherapy. mIF approach is well suited to such a need. The deep profiling and spatial characterization provided by a high-plex mIF assay is a powerful tool to identify predictive biomarkers. And low-plex mIF technology offers a faster turn-around time and could potentially be translated into clinical practice.
In this study, 40 CRC samples were analyzed using MultiOmyxTM and PhenoImagerTM assays. For MultiOmyx analysis, the samples were stained with a comprehensive immunoncology (IO) panel including 17 biomarkers. The expression and spatial distribution of each biomarker was studied with proprietary deep-learning based image analysis. Adjacent sections of each sample were stained by two PhenoImager panels: MOTiF kit and a 5-plex custom assay for identification of mature tertiary lymphoid structures (TLS). Biomarker classification was performed using Indica Halo analytics algorithm.
PhenoImager panels successfully identified different subtypes of tumor infiltrating lymphocytes (TILs) and mature TLS within the TME. MultiOmyx analysis was able to provide a comprehensive characterization of immune markers and further classification of TLS into different maturation stages based on biomarker expression and spatial organization of immune cells. To assess cross-platform concordance, correlation coefficient was calculated using cell density data generated by each platform. Direct correlation was observed for the markers used in this study.
This study provides a use case on complementary mIF platforms to support translational studies at different stages. The data indicates that it can effect a practical path with use of a high-plex mIF assay for discovery of novel biomarkers and then bridge into a low-plex mIF assay to further clinical understanding and practice.
The authors.
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All authors have declared no conflicts of interest.