{"id":2486,"date":"2025-11-04T05:12:02","date_gmt":"2025-11-04T05:12:02","guid":{"rendered":"https:\/\/www.lunit.io\/en\/?post_type=publication&#038;p=2486"},"modified":"2025-11-14T05:15:47","modified_gmt":"2025-11-14T05:15:47","slug":"unveiling-spatial-correlation-between-cell-surface-targets-and-lymphocytes-via-artificial-intelligence-ai-powered-quantitative-immunohistochemistry-ihc-analysis","status":"publish","type":"publication","link":"https:\/\/lunit.supremeclients.com\/ko\/publication\/unveiling-spatial-correlation-between-cell-surface-targets-and-lymphocytes-via-artificial-intelligence-ai-powered-quantitative-immunohistochemistry-ihc-analysis\/","title":{"rendered":"Unveiling spatial correlation between cell surface targets and lymphocytes via artificial intelligence (AI)-powered quantitative immunohistochemistry (IHC) analysis"},"content":{"rendered":"<div class=\"col-span-12 mt-2 bg-white md:mt-0\">\n<div class=\"hero-title\">\n<h3 id=\"article-title-1\" class=\"text-bmj-silver-800 mb-6 text-center text-[28px] font-normal leading-[34px] md:text-left md:text-[38px] md:leading-[48px]\" style=\"text-align: left;\" data-testid=\"title\">Unveiling spatial correlation between cell surface targets and lymphocytes via artificial intelligence (AI)-powered quantitative immunohistochemistry (IHC) analysis<\/h3>\n<\/div>\n<\/div>\n<div class=\"bg-white\">\n<div id=\"xref-fn-1-1\" class=\"relative mb-[20px]\" data-testid=\"author-affiliations-list\"><\/div>\n<\/div>\n<p>Gahee Park, Jiho Park, Sukjun Kim, Sanghoon Song, Hosik Kim, Chang Ho Ahn, Siraj Ali, Chan-Young Ock<\/p>\n<p><strong>SITC, 2025<\/strong><\/p>\n<p><strong>Abstract<\/strong><\/p>\n<div class=\"abstract-section\">\n<p class=\"section-title\"><strong>Background <\/strong>With increasing emphasis on the relationship of lymphocyte distribution with cell surface targets in immunotherapy, a standardized and reproducible analytical Methods is essential. We propose an AI-powered Methods to assess the relationship of lymphocyte distribution with 74 membrane-specific targets in development by quantifying the density of lymphocytes and target expression.<\/p>\n<p class=\"section-title\"><strong>Methods <\/strong>A total of 47,591 cancer and normal tissue IHC images from Human Protein Atlas were analyzed on 74 target genes. The AI model trained on pathologists-annotated whole-slide images (WSIs) analyzed intra-tumoral\/extra-tumoral infiltrating lymphocyte (iTIL\/exTIL) densities and tumor proportion score (TPS) of surface targets using the IHC WSI. iTIL, exTIL densities were compared between the TPS \u22651% and TPS &lt;1% samples according to cancer types (pan-cancer and each cancer types respectively). Positive\/negative correlation with TIL with protein expression are defined as statistically significant (p &lt;0.05) 2-fold increase\/decrease in average TIL in TPS \u22651% samples. Targets not meeting the above criteria were considered non-significantly different.<\/p>\n<p class=\"section-title\"><strong>Results <\/strong>The IHC analyzer examined 174M cells including 109M cancer cells in 34 cancer types. Among 74 targets analyzed, mean TPS of all targets was 17.4%(\u00b1 16.4%), with 49.0% of WSIs (12,939) classified as TPS \u2265 1% and 51.0% (13,443) classified as TPS &lt;1%. In pan-cancer analysis, TIL density was mostly either decreased (23.0%) or not significantly changed (74.3%) in the TPS \u2265 1% group, but only 2 (2.7%) targets including PD-L1 were positively correlated with total TIL (iTIL+exTIL). Moreover, iTIL significantly increased in the tumor tissues with PD-L1 TPS \u2265 1% in pan-cancer analysis (mean iTIL 510.2\/mm2 vs 237.5\/mm2, p &lt;0.001). In subgroup analysis by cancer type, a positive correlation is observed in 24 pairs of target-cancer type. Among these FGFR4 showed positive correlation with iTIL in colorectal cancer (mean iTIL 88.2\/mm2 vs 8.1\/mm2, p = 0.011), non-squamous lung cancer (75.0\/mm2 vs 25.9\/mm2, p = 0.015) and uterine carcinoma (225.4\/mm2 vs 31.7\/mm2, p = 0.003) but not in other cancer types that highly express FGFR4 such as of hepatocellular carcinoma or squamous lung cancer.<\/p>\n<p class=\"section-title\"><strong>Conclusions <\/strong>This correlative pipeline reveals a lack of pan-cancer correlation for cell surface targets and TIL infiltration, with the exception of PD-L1. However, expression of FGFR4 was linked to TIL presence in select cancer types which suggests the possible utility of a FGFR4 T cell engager bi-specific.<\/p>\n<\/div>\n<p style=\"text-align: center;\"><a href=\"https:\/\/jitc.bmj.com\/content\/13\/Suppl_2\/A1250\"><b>View Abstract<\/b><\/a><\/p>\n","protected":false},"featured_media":0,"template":"","publication-oncology":[95,136,85],"publication-region":[91],"publication-type":[],"radiology":[],"class_list":["post-2486","publication","type-publication","status-publish","hentry","publication-oncology-conference-posters","publication-oncology-lunit-scope-uihc","publication-oncology-pan-cancer","publication-region-north-america"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Unveiling spatial correlation between cell surface targets and lymphocytes via artificial intelligence (AI)-powered quantitative immunohistochemistry (IHC) analysis - Lunit<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/lunit.supremeclients.com\/publication\/unveiling-spatial-correlation-between-cell-surface-targets-and-lymphocytes-via-artificial-intelligence-ai-powered-quantitative-immunohistochemistry-ihc-analysis\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Unveiling spatial correlation between cell surface targets and lymphocytes via artificial intelligence (AI)-powered quantitative immunohistochemistry (IHC) analysis - 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