Yeong Hak Bang, Choong-Kun Lee, Kyunghye Bang, Hyung-Don Kim, Kyu-Pyo Kim, Jae Ho Jeong, Inkeun Park, Baek-Yeol Ryoo, Dong Ki Lee, Hye Jin Choi, Taek Chung, Seung Hyuck Jeon, Eui-Cheol Shin, Chiyoon Oum, Seulki Kim, Yoojoo Lim, Gahee Park, Chang Ho Ahn, Taebum Lee, Richard S Finn, Chan-Young Ock, Jinho Shin, Changhoon Yoo
{"title":"人工智能驱动的肿瘤浸润淋巴细胞空间分析作为胆道癌免疫检查点抑制剂的预测性生物标记物。","authors":"Yeong Hak Bang, Choong-Kun Lee, Kyunghye Bang, Hyung-Don Kim, Kyu-Pyo Kim, Jae Ho Jeong, Inkeun Park, Baek-Yeol Ryoo, Dong Ki Lee, Hye Jin Choi, Taek Chung, Seung Hyuck Jeon, Eui-Cheol Shin, Chiyoon Oum, Seulki Kim, Yoojoo Lim, Gahee Park, Chang Ho Ahn, Taebum Lee, Richard S Finn, Chan-Young Ock, Jinho Shin, Changhoon Yoo","doi":"10.1158/1078-0432.CCR-24-1265","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Recently, anti-programmed cell death-1/anti-programmed cell death ligand-1 (anti-PD1/L1) immunotherapy has been demonstrated for its efficacy when combined with cytotoxic chemotherapy in randomized phase 3 trials for advanced biliary tract cancer (BTC). However, no biomarker predictive of benefit has been established for anti-PD1/L1 in BTC. Here, we evaluated tumor-infiltrating lymphocytes (TIL) using artificial intelligence-powered immune phenotype (AI-IP) analysis in advanced BTC treated with anti-PD1.</p><p><strong>Experimental design: </strong>Pretreatment hematoxylin and eosin (H&E)-stained whole-slide images from 339 patients with advanced BTC who received anti-PD1 as second-line treatment or beyond, were employed for AI-IP analysis and correlative analysis between AI-IP and efficacy outcomes with anti-PD1. Next, data and images of the BTC cohort from The Cancer Genome Atlas (TCGA) were additionally analyzed to evaluate the transcriptomic and mutational characteristics of various AI-IP in BTC.</p><p><strong>Results: </strong>Overall, AI-IP were classified as inflamed [high intratumoral TIL (iTIL)] in 40 patients (11.8%), immune-excluded (low iTIL and high stromal TIL) in 167 patients (49.3%), and immune-desert (low TIL overall) in 132 patients (38.9%). The inflamed IP group showed a substantially higher overall response rate compared with the noninflamed IP groups (27.5% vs. 7.7%, P < 0.001). Median overall survival and progression-free survival were significantly longer in the inflamed IP group than in the noninflamed IP group (OS, 12.6 vs. 5.1 months; P = 0.002; PFS, 4.5 vs. 1.9 months; P < 0.001). In the TCGA cohort analysis, the inflamed IP showed increased cytolytic activity scores and IFNγ signature compared with the noninflamed IP.</p><p><strong>Conclusions: </strong>AI-IP based on spatial TIL analysis was effective in predicting the efficacy outcomes in patients with BTC treated with anti-PD1 therapy. Further validation is necessary in the context of anti-PD1/L1 plus gemcitabine-cisplatin.</p>","PeriodicalId":10279,"journal":{"name":"Clinical Cancer Research","volume":" ","pages":"4635-4643"},"PeriodicalIF":10.0000,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial Intelligence-Powered Spatial Analysis of Tumor-Infiltrating Lymphocytes as a Potential Biomarker for Immune Checkpoint Inhibitors in Patients with Biliary Tract Cancer.\",\"authors\":\"Yeong Hak Bang, Choong-Kun Lee, Kyunghye Bang, Hyung-Don Kim, Kyu-Pyo Kim, Jae Ho Jeong, Inkeun Park, Baek-Yeol Ryoo, Dong Ki Lee, Hye Jin Choi, Taek Chung, Seung Hyuck Jeon, Eui-Cheol Shin, Chiyoon Oum, Seulki Kim, Yoojoo Lim, Gahee Park, Chang Ho Ahn, Taebum Lee, Richard S Finn, Chan-Young Ock, Jinho Shin, Changhoon Yoo\",\"doi\":\"10.1158/1078-0432.CCR-24-1265\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>Recently, anti-programmed cell death-1/anti-programmed cell death ligand-1 (anti-PD1/L1) immunotherapy has been demonstrated for its efficacy when combined with cytotoxic chemotherapy in randomized phase 3 trials for advanced biliary tract cancer (BTC). However, no biomarker predictive of benefit has been established for anti-PD1/L1 in BTC. Here, we evaluated tumor-infiltrating lymphocytes (TIL) using artificial intelligence-powered immune phenotype (AI-IP) analysis in advanced BTC treated with anti-PD1.</p><p><strong>Experimental design: </strong>Pretreatment hematoxylin and eosin (H&E)-stained whole-slide images from 339 patients with advanced BTC who received anti-PD1 as second-line treatment or beyond, were employed for AI-IP analysis and correlative analysis between AI-IP and efficacy outcomes with anti-PD1. Next, data and images of the BTC cohort from The Cancer Genome Atlas (TCGA) were additionally analyzed to evaluate the transcriptomic and mutational characteristics of various AI-IP in BTC.</p><p><strong>Results: </strong>Overall, AI-IP were classified as inflamed [high intratumoral TIL (iTIL)] in 40 patients (11.8%), immune-excluded (low iTIL and high stromal TIL) in 167 patients (49.3%), and immune-desert (low TIL overall) in 132 patients (38.9%). The inflamed IP group showed a substantially higher overall response rate compared with the noninflamed IP groups (27.5% vs. 7.7%, P < 0.001). Median overall survival and progression-free survival were significantly longer in the inflamed IP group than in the noninflamed IP group (OS, 12.6 vs. 5.1 months; P = 0.002; PFS, 4.5 vs. 1.9 months; P < 0.001). In the TCGA cohort analysis, the inflamed IP showed increased cytolytic activity scores and IFNγ signature compared with the noninflamed IP.</p><p><strong>Conclusions: </strong>AI-IP based on spatial TIL analysis was effective in predicting the efficacy outcomes in patients with BTC treated with anti-PD1 therapy. Further validation is necessary in the context of anti-PD1/L1 plus gemcitabine-cisplatin.</p>\",\"PeriodicalId\":10279,\"journal\":{\"name\":\"Clinical Cancer Research\",\"volume\":\" \",\"pages\":\"4635-4643\"},\"PeriodicalIF\":10.0000,\"publicationDate\":\"2024-10-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical Cancer Research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1158/1078-0432.CCR-24-1265\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical Cancer Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1158/1078-0432.CCR-24-1265","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
目的：在治疗晚期胆道癌（BTC）的随机三期试验中，抗-PD-1/L1 与细胞毒性化疗联合使用已被证实具有疗效。然而，抗PD-1/L1在BTC中的疗效预测生物标志物尚未确定。在此，我们使用人工智能驱动的免疫表型（AI-IP）分析评估了接受抗PD-1治疗的晚期BTC中的肿瘤浸润淋巴细胞（TILs）：利用339名接受抗-PD-1二线治疗或二线以上治疗的晚期BTC患者的治疗前H&E染色全切片图像进行AI-IP分析，并分析AI-IP与抗-PD-1疗效之间的相关性。此外，还对癌症基因组图谱（TCGA）中的BTC队列数据和图像进行了分析，以评估BTC中各种AI-IP的转录组学和突变特征：总的来说，40 名患者（11.8%）的 AI-IPs 被归类为有炎症（瘤内 TIL [iTIL] 高），167 名患者（49.3%）的 AI-IPs 被归类为免疫排斥（iTIL 低且基质 TIL 高），132 名患者（38.9%）的 AI-IPs 被归类为免疫惰性（总体 TIL 低）。与非炎症 IP 组相比，炎症 IP 组的总体反应率明显更高（27.5% 对 7.7%，PConclusions.Net）：基于空间TIL分析的人工智能驱动IP能有效预测接受抗PD-1治疗的BTC患者的疗效。
Artificial Intelligence-Powered Spatial Analysis of Tumor-Infiltrating Lymphocytes as a Potential Biomarker for Immune Checkpoint Inhibitors in Patients with Biliary Tract Cancer.
Purpose: Recently, anti-programmed cell death-1/anti-programmed cell death ligand-1 (anti-PD1/L1) immunotherapy has been demonstrated for its efficacy when combined with cytotoxic chemotherapy in randomized phase 3 trials for advanced biliary tract cancer (BTC). However, no biomarker predictive of benefit has been established for anti-PD1/L1 in BTC. Here, we evaluated tumor-infiltrating lymphocytes (TIL) using artificial intelligence-powered immune phenotype (AI-IP) analysis in advanced BTC treated with anti-PD1.
Experimental design: Pretreatment hematoxylin and eosin (H&E)-stained whole-slide images from 339 patients with advanced BTC who received anti-PD1 as second-line treatment or beyond, were employed for AI-IP analysis and correlative analysis between AI-IP and efficacy outcomes with anti-PD1. Next, data and images of the BTC cohort from The Cancer Genome Atlas (TCGA) were additionally analyzed to evaluate the transcriptomic and mutational characteristics of various AI-IP in BTC.
Results: Overall, AI-IP were classified as inflamed [high intratumoral TIL (iTIL)] in 40 patients (11.8%), immune-excluded (low iTIL and high stromal TIL) in 167 patients (49.3%), and immune-desert (low TIL overall) in 132 patients (38.9%). The inflamed IP group showed a substantially higher overall response rate compared with the noninflamed IP groups (27.5% vs. 7.7%, P < 0.001). Median overall survival and progression-free survival were significantly longer in the inflamed IP group than in the noninflamed IP group (OS, 12.6 vs. 5.1 months; P = 0.002; PFS, 4.5 vs. 1.9 months; P < 0.001). In the TCGA cohort analysis, the inflamed IP showed increased cytolytic activity scores and IFNγ signature compared with the noninflamed IP.
Conclusions: AI-IP based on spatial TIL analysis was effective in predicting the efficacy outcomes in patients with BTC treated with anti-PD1 therapy. Further validation is necessary in the context of anti-PD1/L1 plus gemcitabine-cisplatin.
期刊介绍：
Clinical Cancer Research is a journal focusing on groundbreaking research in cancer, specifically in the areas where the laboratory and the clinic intersect. Our primary interest lies in clinical trials that investigate novel treatments, accompanied by research on pharmacology, molecular alterations, and biomarkers that can predict response or resistance to these treatments. Furthermore, we prioritize laboratory and animal studies that explore new drugs and targeted agents with the potential to advance to clinical trials. We also encourage research on targetable mechanisms of cancer development, progression, and metastasis.
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