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Abstract 1708: Novel analysis method for in situ spatial phenotyping of cell populations in multimarker imagery


 Spatial biology is the study of tissues within their own context. This has become increasingly important in immuno-oncology because of the need to understand complex tumor microenvironment architecture, especially regarding the immune contexture in and around solid tumors. The main method for understanding the spatial biology of tumors has been the use of multi-marker staining and multiplexed imaging methods in FFPE sections. This imaging data is used to segment the tissue into relevant compartments (tumor, stroma, etc.) and perform multiplex phenotyping of the cells. For highplex data (10+ markers), phenotyping has typically used an unsupervised clustering algorithm. This was a good starting point, but has many limitations, including a reliance on having a sufficient population of cells of each type, variability sample to sample, and no means of differentiating between biologically relevant or impossible phenotypes. Improving cellular phenotyping for highplex samples is a requirement for many of the new multiplexed imaging modalities that are being used.
 We have developed a novel method of phenotyping cells in situ in multiplexed imaging data that involves the biological concept of dividing markers into two user-defined categories: lineage markers and functional markers. Lineage markers are used to identify cellular phenotypes, while functional markers are measured afterwards for their expression levels in across each phenotype and in each individual cell. Lineage markers (e.g., CD45, α-SMA, CD8, cytokeratin, CD20, etc.) define the phenotype of the cell and are essentially binary; they are either present or not present with the correct biodistribution. Functional markers (PD-1, PD-L1, Ki67, etc.), on the other hand, are quantitatively measured in each cell. This new method first uses a supervised classification methodology using lineage markers to find cells with expected phenotypes. In a second step, the levels of functional markers are measured for each cell. Unexpected phenotypes in unclassified cells can then be explored. These data can then be used for downstream spatial/distance/hotspot analysis. During the setup of the analysis the user can explore and QC the data using an interactive data exploration tool which allows for a constant algorithm supervision and correction.
 Here we present data of the novel workflow for 7-plex (Vectra) and 20+-plex (Hyperion/CODEX) data and found that the new method increased the efficiency of setting up complex analysis workflows and improved analysis results. Fewer non-biological phenotypes were found, cell classifications were improved compared to auto-clustering, while still exploring unexpected phenotypes. Interactive exploration of the cell populations enabled quicker validation of results and deeper understanding of the tumor immune contexture. This new method will improve analyses of the next generation of highplex imaging datasets.
 Citation Format: Fabian T. Schneider, James R. Mansfield, Alessandro S. Massaro, Simon Haastrup, Rasmus A. Lyngby, Andreas Hussing, Johan Dore Hansen, Jeppe Thagaard. Novel analysis method for in situ spatial phenotyping of cell populations in multimarker imagery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1708.