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人工智能驱动的肿瘤浸润淋巴细胞空间分析作为免疫检查点抑制剂在胆道癌患者中的潜在生物标志物。

Artificial Intelligence-Powered Spatial Analysis of Tumor-Infiltrating Lymphocytes as a Potential Biomarker for Immune Checkpoint Inhibitors in Patients with Biliary Tract Cancer.

机构信息

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

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

出版信息

Clin Cancer Res. 2024 Oct 15;30(20):4635-4643. doi: 10.1158/1078-0432.CCR-24-1265.

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.

摘要

目的

最近,在随机 III 期临床试验中,抗程序性细胞死亡-1/抗程序性细胞死亡配体-1(抗 PD1/L1)免疫疗法联合细胞毒性化疗在晚期胆道癌(BTC)中显示出疗效。然而,在 BTC 中尚未确定抗 PD1/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)分析评估了接受抗 PD1 治疗的晚期 BTC 中的肿瘤浸润淋巴细胞(TIL)。

实验设计

利用 339 名接受二线或以上抗 PD1 治疗的晚期 BTC 患者的预处理苏木精和伊红(H&E)染色全切片图像,进行 AI-IP 分析,并将 AI-IP 与抗 PD1 治疗的疗效结果进行相关性分析。此外,还对来自癌症基因组图谱(TCGA)的 BTC 队列的数据和图像进行了分析,以评估各种 AI-IP 在 BTC 中的转录组和突变特征。

结果

总体而言,AI-IP 分为 40 名患者(11.8%)的炎症[高肿瘤内 TIL(iTIL)]、167 名患者(49.3%)的免疫排除(低 iTIL 和高基质 TIL)和 132 名患者(38.9%)的免疫荒漠(整体 TIL 低)。与非炎症 IP 组相比,炎症 IP 组的总缓解率显著更高(27.5%对 7.7%,P<0.001)。炎症 IP 组的中位总生存期和无进展生存期明显长于非炎症 IP 组(OS,12.6 对 5.1 个月;P=0.002;PFS,4.5 对 1.9 个月;P<0.001)。在 TCGA 队列分析中,与非炎症 IP 相比,炎症 IP 显示出更高的细胞溶解活性评分和 IFNγ 特征。

结论

基于空间 TIL 分析的 AI-IP 可有效预测接受抗 PD1 治疗的 BTC 患者的疗效结果。在抗 PD1/L1 联合吉西他滨-顺铂的情况下,还需要进一步验证。

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