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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 
Citation
 CLINICAL CANCER RESEARCH, Vol.30(20) : 4635-4643, 2024-10 
Journal Title
CLINICAL CANCER RESEARCH
ISSN
 1078-0432 
Issue Date
2024-10
MeSH
Aged ; Artificial Intelligence* ; Biliary Tract Neoplasms* / drug therapy ; Biliary Tract Neoplasms* / immunology ; Biliary Tract Neoplasms* / pathology ; Biomarkers, Tumor* ; Female ; Humans ; Immune Checkpoint Inhibitors* / pharmacology ; Immune Checkpoint Inhibitors* / therapeutic use ; Lymphocytes, Tumor-Infiltrating* / drug effects ; Lymphocytes, Tumor-Infiltrating* / immunology ; Lymphocytes, Tumor-Infiltrating* / metabolism ; Male ; Middle Aged ; Prognosis ; Programmed Cell Death 1 Receptor / antagonists & inhibitors
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.
Full Text
https://aacrjournals.org/clincancerres/article/30/20/4635/748799
DOI
10.1158/1078-0432.ccr-24-1265
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Lee, Dong Ki(이동기) ORCID logo https://orcid.org/0000-0002-0048-9112
Lee, Choong-kun(이충근) ORCID logo https://orcid.org/0000-0001-5151-5096
Choi, Hye Jin(최혜진) ORCID logo https://orcid.org/0000-0001-5917-1400
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/201218
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