{"id":2484,"date":"2025-11-07T05:00:17","date_gmt":"2025-11-07T05:00:17","guid":{"rendered":"https:\/\/www.lunit.io\/en\/?post_type=publication&#038;p=2484"},"modified":"2026-03-23T14:51:15","modified_gmt":"2026-03-23T05:51:15","slug":"ai-powered-image-based-spatial-profiling-of-met-mutated-non-small-cell-lung-cancer-identifies-immune-active-met-exon-14-skipping-subtypes-as-potential-immunotherapy-targets","status":"publish","type":"publication","link":"https:\/\/www.lunit.io\/en\/publication\/ai-powered-image-based-spatial-profiling-of-met-mutated-non-small-cell-lung-cancer-identifies-immune-active-met-exon-14-skipping-subtypes-as-potential-immunotherapy-targets\/","title":{"rendered":"AI-powered image-based spatial profiling of MET-mutated non-small cell lung cancer identifies immune-active MET exon 14 skipping subtypes as potential immunotherapy targets"},"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\">AI-powered image-based spatial profiling of MET-mutated non-small cell lung cancer identifies immune-active MET exon 14 skipping subtypes as potential immunotherapy targets<\/h3>\n<\/div>\n<\/div>\n<p>Zachary D Wallen, Yoojoo Lim, Cherub Kim, Stephanie B Hastings, Kyle C Strickland, Chris C Oh, Brian J Caveney, Marcia Eisenberg, Eric A Severson, Siraj Ali, Shakti Ramkissoon<\/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>MET alterations are oncogenic drivers in non-small cell lung cancer (NSCLC), but their impact on the tumor microenvironment (TME) remains unclear. Spatial analysis of whole slide images (WSIs) enables high-resolution TME characterization, overcoming limitations of bulk sequencing. This study used AI-powered spatial analysis to profile immune phenotypes and cellular composition across MET mutations in NSCLC.<\/p>\n<p class=\"section-title\"><strong>Methods <\/strong>We retrospectively analyzed 371 H&amp;E-stained WSIs from NSCLC biopsies collected during routine clinical care using the AI-based SCOPE IO algorithm (Lunit) to quantify tumor cellular densities and immune phenotypes (inflamed, immune-excluded, immune-desert). Cases were stratified by MET status: exon 14 skipping (METex14, N=241), amplification (METamp, N=31), and wildtype (METwt, N=99). SCOPE IO metrics and targeted sequencing-based immune gene expression (iGEX) were compared across groups. METex14 tumors were further grouped into inflamed (N=63) and non-inflamed (N=158) subtypes. A machine learning (ML) model trained on iGEX features associated with METex14 subtypes was used to impute subtypes in a second NSCLC cohort with immunotherapy outcomes (N=205).<\/p>\n<p class=\"section-title\"><strong>Results <\/strong>METex14 tumors had more inflamed phenotypes than METamp and METwt (29% vs 10% and 15%, P=2E-3), with higher densities of endothelial cells, fibroblasts, and lymphocytes in cancer areas (P\u22642E-3), elevated inflamed scores (P=0.01), and lower immune-desert scores (P=0.02). METamp tumors showed more immune-desert phenotypes (79% vs 52% and 63%, P=0.01), increased presence of mitotic cells (P=4E-8), fewer non-tumor cells (P&lt;0.05), and higher immune-desert scores (P=0.047). iGEX confirmed these findings: METex14 tumors had higher overall iGEX (192 genes), while METamp tumors showed elevated proliferation-associated genes (18 genes) (P&lt;0.05). Inflamed METex14 tumors had more lymphocytes, macrophages, and other non-tumor cells in cancer and stromal areas (P\u22642E-3), with increased iGEX in immune activation pathways (166 genes, P&lt;0.05). ML-based feature selection identified 46 differentially expressed genes distinguishing METex14 subtypes with high accuracy (ROC-AUC=0.94). In a second cohort, tumors classified as inflamed were associated with improved survival under immunotherapy (HR=0.5, P=0.004) (<a id=\"xref-fig-1-1\" class=\"xref-fig\" href=\"https:\/\/jitc.bmj.com\/content\/13\/Suppl_3\/A1590#F1\">figure 1<\/a>).<\/p>\n<p class=\"section-title\"><strong>Conclusions <\/strong>AI-powered spatial analysis and iGEX profiling revealed distinct TME profiles across MET mutations in NSCLC. METex14 tumors exhibited immune-active TMEs, while METamp tumors were immune-deficient and proliferative. A subset of METex14 tumors showed high immune activity, suggesting potential responsiveness to immunotherapy (<a id=\"xref-fig-2-1\" class=\"xref-fig\" href=\"https:\/\/jitc.bmj.com\/content\/13\/Suppl_3\/A1590#F2\">figure 2<\/a>). These findings highlight MET-driven NSCLC heterogeneity and the utility of spatial AI tools for immunotherapy stratification and biomarker development.<\/p>\n<\/div>\n<p style=\"text-align: center;\"><a href=\"https:\/\/jitc.bmj.com\/content\/13\/Suppl_3\/A1590\"><b>View Abstract<\/b><\/a><\/p>\n","protected":false},"featured_media":0,"template":"","publication-oncology":[95,78,133],"publication-region":[91],"publication-type":[],"radiology":[],"class_list":["post-2484","publication","type-publication","status-publish","hentry","publication-oncology-conference-posters","publication-oncology-lung-cancer","publication-oncology-lunit-scope-io","publication-region-north-america"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>AI-powered image-based spatial profiling of MET-mutated non-small cell lung cancer identifies immune-active MET exon 14 skipping subtypes as potential immunotherapy targets - 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:\/\/www.lunit.io\/en\/publication\/ai-powered-image-based-spatial-profiling-of-met-mutated-non-small-cell-lung-cancer-identifies-immune-active-met-exon-14-skipping-subtypes-as-potential-immunotherapy-targets\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI-powered image-based spatial profiling of MET-mutated non-small cell lung cancer identifies immune-active MET exon 14 skipping subtypes as potential immunotherapy targets - Lunit\" \/>\n<meta property=\"og:description\" content=\"AI-powered image-based spatial profiling of MET-mutated non-small cell lung cancer identifies immune-active MET exon 14 skipping subtypes as potential immunotherapy targets Zachary D Wallen, Yoojoo Lim, Cherub Kim, Stephanie B Hastings, Kyle C Strickland, Chris C Oh, Brian J Caveney, Marcia Eisenberg, Eric A Severson, Siraj Ali, Shakti Ramkissoon SITC, 2025 Abstract Background MET alterations [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.lunit.io\/en\/publication\/ai-powered-image-based-spatial-profiling-of-met-mutated-non-small-cell-lung-cancer-identifies-immune-active-met-exon-14-skipping-subtypes-as-potential-immunotherapy-targets\/\" \/>\n<meta property=\"og:site_name\" content=\"Lunit\" \/>\n<meta property=\"article:modified_time\" content=\"2026-03-23T05:51:15+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@lunit_ai\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.lunit.io\/en\/publication\/ai-powered-image-based-spatial-profiling-of-met-mutated-non-small-cell-lung-cancer-identifies-immune-active-met-exon-14-skipping-subtypes-as-potential-immunotherapy-targets\/\",\"url\":\"https:\/\/www.lunit.io\/en\/publication\/ai-powered-image-based-spatial-profiling-of-met-mutated-non-small-cell-lung-cancer-identifies-immune-active-met-exon-14-skipping-subtypes-as-potential-immunotherapy-targets\/\",\"name\":\"AI-powered image-based spatial profiling of MET-mutated non-small cell lung cancer identifies immune-active MET exon 14 skipping subtypes as potential immunotherapy targets - 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