Abstract 5504: Integrating ultrahigh-plex spatial phenotyping: From discovery to clinical applications

Abstract Background The journey from discovering predictive spatial biomarkers (known as spatial signatures) for immunotherapy responses to their clinical application necessitates a cohesive approach bridging ultrahigh-plex discovery experiments with high-throughput translational studies. This study focuses on integrating Akoya Biosciences spatial multiplexed imaging technologies with advanced data analysis techniques for comprehensive spatial phenotyping across the discovery-to-clinical continuum. Methods Human formalin-fixed, paraffin-embedded (FFPE) cancer tissues were profiled using ultrahigh-plex PhenoCode™ Discovery panels to examine cell lineage, immune activation, and checkpoint markers via the PhenoCycler®-Fusion spatial biology platform. The subsequent use of PhenoCode™ Signature panels targeted specific biomarkers of immune profile, immune contexture, tumor-infiltrating lymphocytes (TIL), macrophage polarization, and T cell status via the PhenoImager® HT platform. Open-source whole slide image analysis software QuPath was employed for precise image analysis, including ROI segmentation, cell detection, classification, exploration of spatial interactions, and identification of distinct spatial signatures. Results Our analysis revealed distinct spatial relationships within various tumor types, quantifying immune cell distributions and their interactions. The ultrahigh-plex data correlated with high-throughput signature panel analyses, thus paving a new simplified way for targeted identification and development of predictive spatial signatures for immunotherapy outcomes. Conclusions The combined use of ultrahigh-plex discovery panels, high-throughput signature panels, and deep-learning image analysis provides a comprehensive understanding of cellular interactions in the tumor microenvironment. This integrated approach, utilizing Akoya's end-to-end workflows, accelerates the identification of predictive spatial biomarker signatures across various human tissue samples. Citation Format: Aditya Pratapa, Ning Ma, Niyati Jhaveri. Integrating ultrahigh-plex spatial phenotyping: From discovery to clinical applications 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 (6Suppl): Abstract nr 5504.