在针对晚期胆道癌 (BTC) 的随机 3 期试验中，抗 PD-1/L1 药物与细胞毒性化疗联合使用的疗效已得到证实。然而，尚未建立预测 BTC 中抗 PD-1/L1 获益的生物标志物。在这里，我们使用人工智能驱动的免疫表型 (AI-IP) 分析来评估经抗 PD-1 治疗的高级 BTC 中的肿瘤浸润淋巴细胞 (TIL)。

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