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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.
Bang, Yeong Hak; Lee, Choong-Kun; Bang, Kyunghye; Kim, Hyung-Don; Kim, Kyu-Pyo; Jeong, Jae Ho; Park, Inkeun; Ryoo, Baek-Yeol; Lee, Dong Ki; Choi, Hye Jin; Chung, Taek; Jeon, Seung Hyuck; Shin, Eui-Cheol; Oum, Chiyoon; Kim, Seulki; Lim, Yoojoo; Park, Gahee; Ahn, Chang Ho; Lee, Taebum; Finn, Richard S; Ock, Chan-Young; Shin, Jinho; Yoo, Changhoon.
Afiliação
  • Bang YH; Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Lee CK; Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea.
  • Bang K; Division of Medical Oncology, Department of Internal Medicine, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Kim HD; Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Kim KP; Department of Hemato-oncololgy, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Republic of Korea.
  • Jeong JH; Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Park I; Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Ryoo BY; Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Lee DK; Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Choi HJ; Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Chung T; Division of Medical Oncology, Department of Internal Medicine, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Jeon SH; Division of Medical Oncology, Department of Internal Medicine, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Shin EC; Department of Pathology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Oum C; Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
  • Kim S; Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
  • Lim Y; Lunit, Seoul, Republic of Korea.
  • Park G; Lunit, Seoul, Republic of Korea.
  • Ahn CH; Lunit, Seoul, Republic of Korea.
  • Lee T; Lunit, Seoul, Republic of Korea.
  • Finn RS; Lunit, Seoul, Republic of Korea.
  • Ock CY; Lunit, Seoul, Republic of Korea.
  • Shin J; Division of Hematology-Oncology, Geffen School of Medicine at UCLA, Los Angeles, California.
  • Yoo C; Lunit, Seoul, Republic of Korea.
Clin Cancer Res ; 30(20): 4635-4643, 2024 Oct 15.
Article em En | MEDLINE | ID: mdl-39150517
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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias do Sistema Biliar / Inteligência Artificial / Biomarcadores Tumorais / Linfócitos do Interstício Tumoral / Inibidores de Checkpoint Imunológico Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias do Sistema Biliar / Inteligência Artificial / Biomarcadores Tumorais / Linfócitos do Interstício Tumoral / Inibidores de Checkpoint Imunológico Idioma: En Ano de publicação: 2024 Tipo de documento: Article