Cited 4 times in
Artificial Intelligence-Powered Spatial Analysis of Tumor-Infiltrating Lymphocytes as a Potential Biomarker for Immune Checkpoint Inhibitors in Patients with Biliary Tract Cancer
DC Field | Value | Language |
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dc.contributor.author | 이동기 | - |
dc.contributor.author | 이충근 | - |
dc.contributor.author | 최혜진 | - |
dc.date.accessioned | 2024-12-06T03:42:47Z | - |
dc.date.available | 2024-12-06T03:42:47Z | - |
dc.date.issued | 2024-10 | - |
dc.identifier.issn | 1078-0432 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/201218 | - |
dc.description.abstract | 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. | - |
dc.description.statementOfResponsibility | restriction | - |
dc.language | English | - |
dc.publisher | American Association for Cancer Research | - |
dc.relation.isPartOf | CLINICAL CANCER RESEARCH | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Aged | - |
dc.subject.MESH | Artificial Intelligence* | - |
dc.subject.MESH | Biliary Tract Neoplasms* / drug therapy | - |
dc.subject.MESH | Biliary Tract Neoplasms* / immunology | - |
dc.subject.MESH | Biliary Tract Neoplasms* / pathology | - |
dc.subject.MESH | Biomarkers, Tumor* | - |
dc.subject.MESH | Female | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Immune Checkpoint Inhibitors* / pharmacology | - |
dc.subject.MESH | Immune Checkpoint Inhibitors* / therapeutic use | - |
dc.subject.MESH | Lymphocytes, Tumor-Infiltrating* / drug effects | - |
dc.subject.MESH | Lymphocytes, Tumor-Infiltrating* / immunology | - |
dc.subject.MESH | Lymphocytes, Tumor-Infiltrating* / metabolism | - |
dc.subject.MESH | Male | - |
dc.subject.MESH | Middle Aged | - |
dc.subject.MESH | Prognosis | - |
dc.subject.MESH | Programmed Cell Death 1 Receptor / antagonists & inhibitors | - |
dc.title | Artificial Intelligence-Powered Spatial Analysis of Tumor-Infiltrating Lymphocytes as a Potential Biomarker for Immune Checkpoint Inhibitors in Patients with Biliary Tract Cancer | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Internal Medicine (내과학교실) | - |
dc.contributor.googleauthor | Yeong Hak Bang | - |
dc.contributor.googleauthor | Choong-Kun Lee | - |
dc.contributor.googleauthor | Kyunghye Bang | - |
dc.contributor.googleauthor | Hyung-Don Kim | - |
dc.contributor.googleauthor | Kyu-Pyo Kim | - |
dc.contributor.googleauthor | Jae Ho Jeong | - |
dc.contributor.googleauthor | Inkeun Park | - |
dc.contributor.googleauthor | Baek-Yeol Ryoo | - |
dc.contributor.googleauthor | Dong Ki Lee | - |
dc.contributor.googleauthor | Hye Jin Choi | - |
dc.contributor.googleauthor | Taek Chung | - |
dc.contributor.googleauthor | Seung Hyuck Jeon | - |
dc.contributor.googleauthor | Eui-Cheol Shin | - |
dc.contributor.googleauthor | Chiyoon Oum | - |
dc.contributor.googleauthor | Seulki Kim | - |
dc.contributor.googleauthor | Yoojoo Lim | - |
dc.contributor.googleauthor | Gahee Park | - |
dc.contributor.googleauthor | Chang Ho Ahn | - |
dc.contributor.googleauthor | Taebum Lee | - |
dc.contributor.googleauthor | Richard S Finn | - |
dc.contributor.googleauthor | Chan-Young Ock | - |
dc.contributor.googleauthor | Jinho Shin | - |
dc.contributor.googleauthor | Changhoon Yoo | - |
dc.identifier.doi | 10.1158/1078-0432.ccr-24-1265 | - |
dc.contributor.localId | A02723 | - |
dc.contributor.localId | A03259 | - |
dc.contributor.localId | A04219 | - |
dc.relation.journalcode | J00564 | - |
dc.identifier.pmid | 39150517 | - |
dc.identifier.url | https://aacrjournals.org/clincancerres/article/30/20/4635/748799 | - |
dc.contributor.alternativeName | Lee, Dong Ki | - |
dc.contributor.affiliatedAuthor | 이동기 | - |
dc.contributor.affiliatedAuthor | 이충근 | - |
dc.contributor.affiliatedAuthor | 최혜진 | - |
dc.citation.volume | 30 | - |
dc.citation.number | 20 | - |
dc.citation.startPage | 4635 | - |
dc.citation.endPage | 4643 | - |
dc.identifier.bibliographicCitation | CLINICAL CANCER RESEARCH, Vol.30(20) : 4635-4643, 2024-10 | - |
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