유창훈 (울산대학교 의과대학 서울아산병원 종양내과)

한빛사논문

조회119

울산대학교 의과대학 서울아산병원 종양내과

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Clin. Cancer Res., Aug 16 2024, | https://doi.org/10.1158/1078-0432.CCR-24-1265 Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes as a predictive biomarker for immune checkpoint inhibitors in biliary tract cancer

Yeong Hak Bang^1,2§, Choong-kun Lee^3§, Kyunghye Bang^1,4, Hyung-Don Kim¹, Kyu-pyo Kim¹, Jae Ho Jeong¹, Inkeun Park¹, Baek-Yeol Ryoo¹, Dong Ki Lee³, Hye Jin Choi³, Taek Chung⁵, Seung Hyuck Jeon⁶, Eui-Cheol Shin⁶, Chiyoon Oum⁷, Seulki Kim⁷, Yoojoo Lim⁷, Gahee Park⁷, Chang Ho Ahn⁷, Taebum Lee⁷, Richard S. Finn⁸, Chan-Young Ock⁷, Jinho Shin^9*, Changhoon Yoo^1*

¹Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea

²Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Korea.

³Division of Medical Oncology, Department of Internal Medicine, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Korea.

⁴Department of Hemato-oncololgy, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea.

⁵Department of Pathology, Yonsei Cancer Center, Yonsei University College of Medicine, Korea

⁶Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea

⁷Lunit, Seoul, Republic of Korea

⁸Division of Hematology-Oncology, Geffen School of Medicine at UCLA, Los Angeles, CA, USA

⁹Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea

^*Corresponding authors: Changhoon Yoo, MD, PhD, Jinho Shin, MD

^§These two authors contributed equally as co-first authors.

Abstract

Purpose: Anti-PD-1/L1 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-PD-1/L1 in BTC. Here, we evaluated tumor-infiltrating lymphocytes (TILs) using artificial intelligence-powered immune phenotype (AI-IP) analysis in advanced BTC treated with anti-PD-1.

Patients and methods: Pre-treatment H&E-stained whole-slide images from 339 advanced BTC patients who received anti-PD-1 as second-line treatment or beyond, were utilized for AI-IP analysis and correlative analysis between AI-IP and efficacy outcomes with anti-PD-1. Next, data and images of BTC cohort from The Cancer Genome Atlas (TCGA) were additionally analyzed to evaluate the transcriptomic and mutational characteristics of various AI-IPs in BTC.

Results: Overall, AI-IPs were classified as inflamed (high intratumoral TIL [iTIL]) in 40 patients (11.8%), immune-excluded (low iTIL and high stromal TIL) in 167 (49.3%), and immune-deserted (low TIL overall) in 132 (38.9%). The inflamed IP group showed a significantly higher overall response rate compared to the non-inflamed 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 non-inflamed IP group (OS: 12.6 vs. 5.1 months, P=0.002; PFS: 4.5 vs. 1.9 months, P<0.001). In the analysis using TCGA cohort, the inflamed IP showed increased cytolytic activity scores and an interferon-gamma signature compared to the non-inflamed IP.

Conclusions: AI-powered IP based on spatial TIL analysis was effective in predicting the efficacy outcomes in patients with BTC treated with anti-PD-1.

논문정보

형식Research article
게재일2024년 08월 (BRIC 등록일 2024-08-19)
연구진국내 연구진
분야 의약학 > 응용의학

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