Abstract 5506: HOST-Factor: A comprehensive platform for single-cell highplex immunofluorescence staining, digital imaging, spatial mapping, and quantitative analysis of the tumor microenvironment
Authors: Franco-Barraza Janusz , Schneider Fabian , Norré-Sørensen Rasmus , Ahrenkiel-Lyngby Rasmus , Brown Caneta , Winkowski Dan , Mansfield James Robert , Cukierman Edna 
Published: March 2024

Solid tumor complexity extends beyond the genetic and functional landscapes of heterogenous cancer cells, encompassing the tumor microenvironment (TME). Elucidating the TME’s complexity requires a comprehensive assessment of its cellular composition, functional states, and spatial distributions. We developed the Harmonic Output of Stromal Traits (HOST) to identify TME cells, and the HOST-Factor to quantify their functional states. The HOST-Factor is a numerical value that reflects the relative contribution of cancer-associated fibroblasts (CAFs) to tumor-suppressive or tumor-promoting functions.

Our workflow combines automated cycling highplex immunofluorescent microscopy with artificial intelligence (AI)-guided image analysis. This generates HOST-Factor values for each identified TME cell within selected regions of interest, providing spatial distribution data. The TME signature encompasses 15 immune cells and 14 CAF antibody-detected biomarkers.

We applied our workflow to ten human pancreatic cancer specimens, generating OME-TIFF output images. This cancer model was used due to its significant TME makeup. The 29 highplex AI-based digital image analysis was conducted using the Phenoplex™ workflow from Visiopharm. The workflow included deep-learning-based tissue morphologic and cellular segmentations, cellular phenotyping, and integration of spatial/location data. Biomarker subsets were visualized, and a user-trained algorithm was used for tissue segmentation. Nuclear segmentation was done using a pre-trained algorithm on a DAPI-labeled DNA channel. Cellular phenotyping was performed using thresholds based on visual assessment and confirmation of positivity. Spatial neighborhood plots and metrics, heatmaps and partitioned t-SNE plots were generated for the dataset for downstream analysis. Importantly, the workflow's visualization templates, pre-trained nuclear/cytoplasm segmentation tools, and neighborhood plots and metrics, are reusable and fully customizable for new datasets.

Using HOST-Factor values, we successfully classified cancer and TME cells, along with their functional states, and spatial distributions.

This AI-based computational approach and user-friendly workflow provides a simple and effective way to obtain single-cell information from multiplex immunofluorescence images, making spatial phenotyping of several cell populations in tissues more accessible to researchers, providing a fully amendable means for future clinical translation.

Citation Format: Janusz Franco-Barraza, Fabian Schneider, Rasmus Norré-Sørensen, Rasmus Ahrenkiel-Lyngby, Caneta Brown, Dan Winkowski, James Robert Mansfield, Edna Cukierman. HOST-Factor: A comprehensive platform for single-cell highplex immunofluorescence staining, digital imaging, spatial mapping, and quantitative analysis of the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5506.