Abstract 2352: Development of an AI-based algorithm to quantify eosinophils in H&amp;E images from colorectal cancer (CRC) tissue sections guided by biomarker staining using multiplex immunofluorescence imaging
Authors: Arezoo Hanifi, Elizabeth Blain, James Hargrove, Jeff Lock, Nam Tran, Vladislav Chizhevsky, Qingyan Au
Journal: Cancer Research (2024)

Abstract Background: Eosinophils are innate immune granulocytes that migrate to areas of inflammation to combat against infection and disease. Best known for their detrimental role in asthma and allergic disorders, there is growing interest in the involvement of eosinophils in cancer. Eosinophils are routinely observed in the tumor microenvironment (TME) and, depending on the cancer type, have been shown to drive other immune cells to either suppress or promote tumor growth - In colorectal cancer (CRC), eosinophil infiltration into the TME has been linked to a favorable prognosis. However, the behavior of eosinophils and their effect on associated immune mediators in the TME remains poorly understood. Currently, eosinophils are primarily identified from H&amp;E stained tissue sections based on morphological features by a pathologist, but it can be challenging to reliably and efficiently identify all eosinophils based on morphology alone. Here, we present a novel image analysis workflow that established an AI-based cell classifier which can accurately quantify eosinophils in H&amp;E stained CRC tissue sections by leveraging biomarker staining of eosinophils using multiplex immunofluorescence (mIF) imaging in conjunction with the morphological characteristics of eosinophils observed by H&amp;E. Methods: CRC tissue sections were labeled with a 5plex panel of eosinophil-specific markers and imaged by mIF using the PhenoImager HT platform (Akoya). Biomarker fluorescence was then quenched, sections stained by H&amp;E, and reimaged using the PhenoImager. mIF and H&amp;E images were then imported into the HALO® platform for algorithm development. Halo AI cell classification of eosinophils was trained on the morphological features of eosinophil staining in H&amp;E images, guided by eosinophil-specific labeling in the 5plex mIF images. H&amp;E staining was also performed on a serial section of each specimen. Morphological identification of eosinophils in H&amp;E images was performed by a pathologist. Results: Correlation analysis was performed to evaluate the relationship, per high power field (HPF), between manual eosinophil counts by a pathologist and AI algorithm derived eosinophils counts. The results showed the two methods are highly correlated, demonstrating reliable algorithm performance. Conclusion: The AI-based eosinophil detection algorithm established here enables high-throughput analysis and quantification of eosinophils from H&amp;E stained CRC tissue specimens and facilitates morphology diagnosis. Citation Format: Arezoo Hanifi, Elizabeth Blain, James Hargrove, Jeff Lock, Nam Tran, Vladislav Chizhevsky, Qingyan Au. Development of an AI-based algorithm to quantify eosinophils in H&amp;E images from colorectal cancer (CRC) tissue sections guided by biomarker staining using multiplex immunofluorescence imaging [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 2352.