Quantifying Immuno-Oncology Markers Across Multiple Tissue Sections With Digital Image Analysis

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Immuno-oncology (IO) approaches require understanding of the types of immune cells present in the tumor microenvironment (TME) as well as the precise localization of these immune cells. However, the acquisition of spatial IO information is technically challenging, due to the requirement for multiplex labeling of immune cells and the need to categorize their location and biomarker content simultaneously. Additionally, the multiplex biomarker panel must be engineered in advance based on a priori assumptions about the correct marker combinations and their location (such as the tumor epithelial nests, TME, or specifically the tumor-stroma interface). This limits the ability to implement novel, scientifically driven assessments into an existing clinical trial which already has defined immunohistochemistry (IHC) endpoints.

To meet the needs of IO clinical trials, Flagship Biosciences has developed a proprietary image analysis platform, FACTS™ (Feature Analysis on Consecutive Tissue Sections), to deduce both the necessary multiplex staining information and the spatial context of IO markers based on the utilization of existing monochrome or multiplex stained slides from a clinical trial. In this study, we demonstrate the utility of this approach to deliver biologically relevant endpoints important for IO clinical trials. First, we performed single IO biomarker staining for CD4, CD8 and FoxP3 in colorectal cancer patient samples and developed a novel image analysis approach that allowed for the accurate quantification of multiple IO markers within specific margins of the TME across multiple tissue sections. For each biomarker the positive cell populations were binned based on distance from the tumor-TME boundary (i.e. % positive cells within 50μm of the tumor-TME boundary). Next, we used computational alignment for evaluating the regional co-localization of multiple immune cell biomarkers from serial sections with single stains for CD4, CD8 or FoxP3.

We demonstrate that the information extracted from multiple single stained serial sections can be used to generate regional co-localization information for multiple IO markers in a given patient sample. Lastly, we developed multiplex chromogenic assays for these same markers, analyzed the multiplex-stained slides with image analysis, and derived the same analysis endpoints for the multiplex slides to the digitally aligned individually labeled sections. The study found that similar interpretation of the inflammatory landscape was possible with multiplex-stained slides and digital alignment of monoplex-stained slides. In summary, this study demonstrated a novel image analysis approach that allows for quantification of IO markers utilizing clinically relevant wet assay technologies and existing IHC stained slides derived from an IO clinical trial.

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