Abstract
Abstract Background: Precise and actionable assessment of cellular mechanisms and interactions within the tumor microenvironment (TME) is critical for advancing clinical trials, preclinical research, and informed decision-making in immuno-oncology. Multiplex immunofluorescence (mIF) assays have emerged as pivotal tools that can enable comprehensive visualization of protein expression and cellular interactions within the TME. Integration of advanced digital analysis tools and mIF assays, can revolutionize our ability to interpret the TME and usher these assays into high-throughput clinical settings. Methods: In this study, we processed tumor samples utilizing Akoya's PhenoCode Signature Immune Profile Human Protein Panel, which includes immune profiling markers CD8, CD68, CD3, CD20 and tumor marker PanCK. Following the Akoya Opal staining protocol, formalin-fixed paraffin-embedded (FFPE) samples were stained and scanned using the PhenoImager HT scanner. The images were then analyzed using Reveal’s AI-mIF digital assay on the ImageDx platform to generate contextual and spatial biomarker data. A deep learning-based cell segmentation model based on MaskRCNN architecture, signal preprocessing for noise and autofluorescence, and dynamic intensity normalization were used. Phenotype classification was precisely executed using dynamic thresholding or neural network classifiers as needed. Results were validated by establishing concordance with pathologist readouts. Results: The application of our methodology yielded highly concordant results when compared with manual counts, demonstrating the efficacy of our approach. The R2 correlation coefficient across all markers passed the minimum criteria of R2>0.85 with regression slopes between 0.7-1.3, indicating a robust alignment with traditional pathologist assessments. We showcase stratifying samples Desert, Excluded, or Inflamed phenotypes and other quantitative and spatial results such as interactions of immune cells and tumor within 30-um neighborhoods, and their dispersion and arrangement in the TME. Conclusion: The integration of Reveal Biosciences' ImageDx platform and RevealAI-mIF provides an ecosystem to host, visualize, and analyze Akoya’s immune profiling images. This collaborative effort has enabled the transformation of complex multiplex imaging data into distinct spatial phenomic endpoints, thereby introducing the potential to significantly advance biomarker discovery and contribution to translational and clinical research in the field of immuno-oncology. Citation Format: Misagh Naderi, Lazar Krstic, Djordje Cikic, Kristen Ruf, Igor Mihajlovic, Sinisa Todorovic, Claire Weston. Images to insights: Transforming TME characterization into actionable spatial metrics with AI-powered multiplex immunofluorescence [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 6878.
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