- Manual pathology assessment is challenging and variable, limiting the data interpretation required for meaningful spatial biology analysis
- Unlike manual assessment, artificial intelligence and machine learning technologies analyze the complex interactions between tumor and immune microenvironments
- This poster illustrates how our IA technology overcomes the shortcomings of manual scoring of CD8+ immune cells in tissue samples
- Understanding CD8 presence in the context of the total immune infiltrate suggests that higher presence of CD8-immune cells, along with a higher CD4+ population, led to an immunosuppressive effect
Manual pathology assessments of Immunohistochemistry (IHC) markers in immuneoncology (IO) is often challenging and results can be highly variable. Measuring biomarker presence in IO must take in to account both immune and tumor environments and provide contextual information on the interaction between tumor and immune biomarker landscapes. Due to the complex nature surrounding tissue biomarker interpretation in IO, digital image analysis (IA) solutions have been developed that layer complex artificial intelligence (AI) and machine learning algorithms to obtain full tissue biomarker profiles necessary for drug development and patient stratification.
Here, a comprehensive tissue analysis solution is presented in monoplex PD-L1 and CD8 stained slides that includes precise digital biomarker scoring in tumor and stromal compartments, recapitulation of common scoring paradigms, analysis of biomarker expression at the tumor/stroma interface (margin), and quantifi cation, scoring, and spatial localization of lymphocytes in the tumor and stroma. Aggregation of all cellular and biomarker data generates tissue phenotypes that characterize the IO landscape of each tissue.