Abstract 3137: Characterizing heterogeneity of CD8 inflammation in biopsied tumor tissues using novel image analysis techniques

Abstract

Abstract Analyzing images of tissue sections stained with biomarker-specific indicators can generate quantitative, cell-specific expression data rather than relying on pathologist's ability to count cells and recapitulate confounding scoring paradigms. Beyond biomarker expression, numerous cell features can be measured such as the cell morphology, biomarker staining patterns and intensity, and localization of the cells within defined spatial compartments or relations to one another. One of the most studied immune cells in immuno-oncology (IO) is the cytotoxic T cell, whose biological function is to identify and destroy infected or dysfunctional cells; this function is complicated by numerous factors within the tumor microenvironment (TME). Tumor cells can aberrantly express immune checkpoint molecules designed to stop CD8 T cells from performing their tumor killing function. Additionally, CD8 T cell function may be perturbed by other immune modulating factors within the TME. IO drugs modulating tumor and immune cell interactions such as PD-L1 checkpoint inhibitors have shown that an inflammatory TME, represented by high CD8 presence in the tumors (inflamed tumors), is indicative of a better therapeutic response. Investigation of CD8 T cell status in biopsied tissues typically describes each tissue as one of three main phenotypes: Immune Desert, Immune Excluded, or Inflamed. Immune Desert tissues do not express appreciable levels of CD8 throughout the tissue. Immune Excluded tissues contain CD8, but the expression is almost exclusively localized to the stroma surrounding tumor nests. Inflamed tissues show higher percentages of CD8 within the tumor nests. While this phenotypic categorization is informative, CD8 expression is often calculated as a mean of expression through the tissue and does not take in to account the heterogeneous nature of tumor biology. This may result in a tumor containing one highly inflamed tumor nest being averaged out with multiple deserted tumor nests and a tissue categorized as excluded or deserted even though inflammation is present. To better represent the heterogeneity of inflammation within tumor tissues, we present an image analysis-based algorithm which not only separates out the tumor, stroma, and tumor/stroma margin, but identifies each tumor nest within the tissue as its own discrete object. This allows for the enumeration of number and size of all tumor nests within the tissue. Each tumor nest is given its own phenotypic classification (inflamed, excluded, or deserted) and the percentage of tumor nests displaying each phenotype. We demonstrate heterogeneity of inflammation assessment alongside standard mean phenotypic evaluations of CD8 expression in non-small cell lung cancer, bladder, and melanoma tumor samples. Practical use in clinical studies can help uncover response or resistance associated phenotypes related to tumor heterogeneity. Citation Format: Charles William Caldwell, Will Paces, Sofia Reitsma, Christopher Brueckner, Si T. Lee-Hoeflich, Roberto Gianani. Characterizing heterogeneity of CD8 inflammation in biopsied tumor tissues using novel image analysis techniques [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3137.