POSTER: Characterizing the Heterogeneity of CD8 Inflammation in Biopsied Tumor Tissues Using Novel Image Analysis Techniques (Poster No. 3137 #AACR2021 in partnership with Scholar Rock)

Abstract

One of the most studied immune cells in IO is the cytotoxic CD8 T cell, whose biological function is to identify and destroy infected or dysfunctional cells. The function of the CD8 T cell 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, the function of CD8 T cells 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 rate.

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 phenotypes 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 of the tissues. While this phenotypic categorization is informative, these percentages of expression are often calculated as a mean of expression through the tissue and does not take into 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, and further quantifies the percentage of CD8 expression within and outside of each tumor nest. Each tumor nest is given its own phenotypic classification of inflamed, excluded, or deserted, and the percentage of tumor nests displaying each phenotype. Within this study, 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.

Conclusions

Cellular data which simply averages CD8 expression across the entirety of a tissue section does not accurately represent the amount of inflammation-associated heterogeneity present in tumor samples.

  • Identifying tumor area objects (tumor nests) within biopsied sections allows for the localization of biomarker expression within and outside of individual tumor nests
  • Representing biomarker data by tumor nest object, rather than averaging cells across the tissue, allows one to assess the inherent heterogeneity within each individual tissue section
  • Collecting tissue-level data on tumor nest inflammation can represent the heterogeneity of inflammation across entire cohorts of tissue
  • The described method can identify tumor areas which can be further investigated via protein or spatial genomic interrogations to understand mechanisms behind tumor nest phenotypes and/or resistance to inflammation. Incorporation into clinical trials will provide better insights into drug efficacy and resistance.

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