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作者

Shanshan Wang, Zhengguang Guo, Boyu Sun, Kai Liu, Jiashuo Chao, Ziyu Xun, Yunchao Wang, Zibo Xu, Ziyue Huang, Hao Wang, Yang Tan, Nan Zhang, Mingjian Piao, Longhao Zhang, Chengjie Li, Shuofeng Li, Jiongyuan Li, Haidan Sun, Feng Qi, Aiwei Wang, XiaoBo Yang, Chengpei Zhu, Hanping Wang, Wei Sun, Haitao Zhao

作者单位

com pumchzcp@12 com yangxiaobo67@pumch.cn. ⁹ Proteomics Research Center, Core Facility of Instrument, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China zhaoht@pumch.cn sunwei@ibms.pumc.edu.cn wanghanping78@16 com yangxiaobo67@pumch.cn. com yangxiaobo67@pumch.cn. ⁷ Department of General Surgery Center, Beijing Youan Hospital, Clinical Center for Liver Cancer, Capital Medical University, Beijing, China zhaoht@pumch.cn sunwei@ibms.pumc.edu.cn wanghanping78@16 com yangxiaobo67@pumch.cn. ⁸ Division of Pulmonary and Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC), Beijing, China zhaoht@pumch.cn sunwei@ibms.pumc.edu.cn wanghanping78@16 Affiliations ¹ Department of Liver Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, China. ² Department of Gastrointestinal Surgery, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. ³ Proteomics Research Center, Core Facility of Instrument, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China. ⁴ Department of Hepatobiliary and Pancreatic Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China. ⁵ Organ Transplantation Center, The First Affiliated Hospital of Shandong First Medical University, Jinan, Shandong, China. ⁶ Department of Liver Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, China zhaoht@pumch.cn sunwei@ibms.pumc.edu.cn wanghanping78@16

刊名

Gut

年份

2025

ISSN

1468-3288

关键词

BIOMARKERS HEPATOBILIARY CANCER IMMUNOTHERAPY PROTEOMICS.

摘要

Background: Most patients with biliary tract cancer do not derive durable clinical benefit from immune checkpoint inhibitors , underscoring the urgent need for predictive biomarkers. While urinary proteomics represents a non-invasive approach for biomarker discovery and mechanism exploration, its utility in ICI-treated patients with cancer remains unexplored. Objective: We aimed to establish urinary proteomics as a predictive tool for ICI responsiveness and to elucidate its relationship with t...更多

Background: Most patients with biliary tract cancer do not derive durable clinical benefit from immune checkpoint inhibitors , underscoring the urgent need for predictive biomarkers. While urinary proteomics represents a non-invasive approach for biomarker discovery and mechanism exploration, its utility in ICI-treated patients with cancer remains unexplored. Objective: We aimed to establish urinary proteomics as a predictive tool for ICI responsiveness and to elucidate its relationship with tumour dynamics and tumour microenvironment remodelling in BTC. Design: We performed a staged mass spectrometry -based discovery-validation proteomics workflow in 211 urine samples from 97 treatment-naïve patients with BTC undergoing ICI-based therapy. A machine learning model was developed based on baseline proteomic features for ICI response prediction. Single-cell transcriptomics of 11 pretreatment tumour biopsies and spatial transcriptomics were integrated to explore the link between urinary proteomics and TME. Results: Patients achieving DCB exhibited enrichment of immune activation and systemic inflammatory pathways, whereas non-durable benefit was correlated with protumourigenic processes. Longitudinal urinary proteomic dynamics could mirror TME remodelling and tumour evolution. A machine learning-derived 4-urinary protein panel , SUB1, MICAL-L1, VARS1) robustly predicted DCB and early responses. Subsequent external validation in an independent cohort using parallel reaction monitoring-MS further confirms its generalisability. PTPN13+ malignant cells were identified as key regulators of proapoptotic TME states, contributing to sustained ICI responsiveness. Conclusions: This study pioneers the application of urinary proteomics in immuno-oncology, providing a non-invasive approach to predict and monitor ICI responsiveness, while offering mechanistic insights into TME dynamics in BTC.收起

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