{"id":1839,"date":"2022-06-02T13:46:21","date_gmt":"2022-06-02T13:46:21","guid":{"rendered":"https:\/\/www.lunit.io\/publication\/the-inflamed-immune-phenotype-iip-a-clinically-actionable-artificial-intelligence-ai-based-biomarker-predictive-of-immune-checkpoint-inhibitor-ici-outcomes-across-16-primary-tumor-types\/"},"modified":"2025-11-01T05:32:59","modified_gmt":"2025-11-01T05:32:59","slug":"the-inflamed-immune-phenotype-iip-a-clinically-actionable-artificial-intelligence-ai-based-biomarker-predictive-of-immune-checkpoint-inhibitor-ici-outcomes-across-16-primary-tumor-types","status":"publish","type":"publication","link":"https:\/\/www.lunit.io\/en\/publication\/the-inflamed-immune-phenotype-iip-a-clinically-actionable-artificial-intelligence-ai-based-biomarker-predictive-of-immune-checkpoint-inhibitor-ici-outcomes-across-16-primary-tumor-types\/","title":{"rendered":"The inflamed immune phenotype (IIP): A clinically actionable artificial intelligence (AI)-based biomarker predictive of immune checkpoint inhibitor (ICI) outcomes across >16 primary tumor types"},"content":{"rendered":"<h3>The inflamed immune phenotype (IIP): A clinically actionable artificial intelligence (AI)-based biomarker predictive of immune checkpoint inhibitor (ICI) outcomes across &gt;16 primary tumor types<\/h3>\n<p>Jeanne Shen, Yoon-La Choi, Taebum Lee, Hyojin Kim, Young Kwang Chae, Benjamin Dulken, Stephanie Bogdan, Maggie Huang, George A. Fisher, Jr., Sehhoon Park, Se-Hoon Lee, Jun-Eul Hwang, Jin-Haeng Chung, Leeseul Kim, Seunghwan Shin, Yoojoo Lim, Heon Song, Sergio Pereira, Chan-Young Ock<\/p>\n<p><strong>ASCO, 2022<\/strong><\/p>\n<p><strong>Background:<\/strong> The IIP, defined by enriched intratumoral tumor-infiltrating lymphocytes (TIL), is a potential tumor-agnostic biomarker of responsiveness to ICI therapy. Here, we validate the IIP, as assessed by Lunit SCOPE IO, an AI-powered spatial TIL analyzer that runs on routine H&amp;E-stained whole-slide images (WSI), for clinical outcome prediction in a large, multi-center international cohort of ICI-treated patients, demonstrating its utility as a practical biomarker to guide ICI treatment planning.<\/p>\n<p><strong>Methods:<\/strong> Lunit SCOPE IO was developed using 17,849 H&amp;E WSI of multiple cancer types, annotated by 104 board-certified pathologists (13.5 x 109 \u00b5m2 area and 6.2 x 106 TIL). IIP+ tumors were defined as those with \u2265 20% of all 1 mm2 tumor tiles in a WSI classified as having a high intratumoral TIL density. We evaluated the correlation between IIP and ICI treatment outcomes (overall response rate (ORR) and progression-free survival (PFS), assessed by RECIST v1.1) in a real-world dataset of 1,806 patients ( &gt; 16 primary tumor types) retrospectively collected from Stanford University Medical Center, Samsung Medical Center, Chonnam National University Hospital, Seoul National University Bundang Hospital, and Northwestern University. IIP status was sub-analyzed by PD-L1 22C3 tumor proportion score (TPS, n = 798), microsatellite status, and tumor mutational burden (TMB, n = 130).<\/p>\n<p><strong>Results:<\/strong> The IIP+ phenotype (35.2%, 636 of 1,806) was highly enriched in nasopharyngeal carcinoma (68.0%), melanoma (56.3%), renal cell carcinoma (52.9%), and non-small cell lung cancer (NSCLC, 33.7%). The IIP+ proportion by PD-L1 TPS ( &lt; 1% \/ \u2265 1%) was 21.6% and 40.7%, respectively. While 33.3% of microsatellite unstable (MSI-H) or TMB-high (\u2265 10\/Mb) tumors were IIP+, a substantial proportion (26.1%) of microsatellite stable (MSS), TMB-low tumors were IIP+. The ORR in IIP+ patients was significantly higher (26.0% vs. 15.8% in IIP-, p &lt; 0.001). Median PFS for IIP+ was 5.3 months (95% CI 4.6-6.9 m), significantly longer than that for IIP- (3.1 m, 95% CI 2.8-3.6 m), with a hazard ratio (HR) of 0.68 (95% CI 0.61-0.76, p &lt; 0.001). The association held after excluding NSCLC patients (n = 909) (HR 0.69, 95% CI 0.59-0.81, p &lt; 0.001). On subgroup analysis, IIP+ correlated significantly with prolonged PFS, regardless of ICI regimen (mono \/ combo therapy) or PD-L1 TPS ( &lt; 1% \/ \u2265 1%). Of note, IIP+ was predictive of favorable PFS only in the MSS, TMB-low group (n = 88, HR 0.56, 95% CI 0.33-0.96), but not in the MSI-H or TMB-high groups.<\/p>\n<p><strong>Conclusions:<\/strong> The IIP, as evaluated by Lunit SCOPE IO, may represent a practical, clinically-actionable biomarker predictive of favorable ICI treatment outcomes across diverse cancer patient populations, including those with PD-L1 negative, MSS\/TMB-low tumors, in whom predictive biomarkers are urgently needed.<\/p>\n<p style=\"text-align: center;\"><a href=\"https:\/\/ascopubs.org\/doi\/abs\/10.1200\/JCO.2022.40.16_suppl.2621\"><strong>View abstract<\/strong><\/a><\/p>\n","protected":false},"featured_media":0,"template":"","publication-oncology":[95,133,85,77,93],"publication-region":[],"publication-type":[],"radiology":[],"class_list":["post-1839","publication","type-publication","status-publish","hentry","publication-oncology-conference-posters","publication-oncology-lunit-scope-io","publication-oncology-pan-cancer","publication-oncology-tumor-type","publication-oncology-type-of-evidence"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>The inflamed immune phenotype (IIP): A clinically actionable artificial intelligence (AI)-based biomarker predictive of immune checkpoint inhibitor (ICI) outcomes across &gt;16 primary tumor types - 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\/the-inflamed-immune-phenotype-iip-a-clinically-actionable-artificial-intelligence-ai-based-biomarker-predictive-of-immune-checkpoint-inhibitor-ici-outcomes-across-16-primary-tumor-types\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The inflamed immune phenotype (IIP): A clinically actionable artificial intelligence (AI)-based biomarker predictive of immune checkpoint inhibitor (ICI) outcomes across &gt;16 primary tumor types - Lunit\" \/>\n<meta property=\"og:description\" content=\"The inflamed immune phenotype (IIP): A clinically actionable artificial intelligence (AI)-based biomarker predictive of immune checkpoint inhibitor (ICI) outcomes across &gt;16 primary tumor types Jeanne Shen, Yoon-La Choi, Taebum Lee, Hyojin Kim, Young Kwang Chae, Benjamin Dulken, Stephanie Bogdan, Maggie Huang, George A. 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