Dynamic urinary proteomics integrates single-cell and spatial transcriptomics to reveal tumour microenvironment and predict immunotherapy response in biliary tract cancer.

Journal: Gut
Published Date: Jun 9, 2026

Abstract

BACKGROUND: Most patients with biliary tract cancer (BTC) do not derive durable clinical benefit (DCB) from immune checkpoint inhibitors (ICIs), 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 (TME) remodelling in BTC. DESIGN: We performed a staged mass spectrometry (MS)-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 (protein tyrosine phosphatase non-receptor 13 (PTPN13), SUB1, MICAL-L1, VARS1) robustly predicted DCB and early responses. Subsequent external validation in an independent cohort (n=24) 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.

Authors

  • Shanshan Wang
    Key Laboratory of Agri-food Safety and Quality, Institute of Quality Standard and Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Ministry of Agriculture of China, Beijing, 100081, PR China.
  • Zhengguang Guo
    Proteomics Research Center, Core Facility of Instruments, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100045, China.
  • Boyu Sun
    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.
  • Kai Liu
    College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China.
  • Jiashuo Chao
    Department of Hepatobiliary and Pancreatic Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
  • Ziyu Xun
    State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences-Beijing (PHOENIX Center), Beijing Institute of Lifeomics, Beijing, China.
  • Yunchao Wang
    Organ Transplantation Center, The First Affiliated Hospital of Shandong First Medical University, Jinan, Shandong, China.
  • Zibo Xu
    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.
  • Ziyue Huang
    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.
  • Hao Wang
    Department of Cardiology, Second Medical Center, Chinese PLA General Hospital, Beijing, China.
  • Yang Tan
    Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China.
  • Nan Zhang
    Department of Pulmonary and Critical Care Medicine II, Emergency General Hospital, Beijing, China.
  • Mingjian Piao
    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.
  • Longhao Zhang
    Department of Medical Discipline Construction, West China hospital of Sichuan University, Chengdu 610041, P. R. China.
  • Chengjie Li
    Nephropathy Center of Integrated Traditional Chinese Medicine and Western Medicine, ZhuJiang Hospital, Southern Medical University, Guangzhou, 510280, China.
  • Shuofeng Li
    Department of Basic Medical Sciences, North China University of Science and Technology, Tangshan 063000, China. [email protected].
  • Jiongyuan Li
    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.
  • Haidan Sun
    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.
  • Feng Qi
    Department of Gastrointestinal Surgery, Tianjin Medical University General Hospital, Tianjin, China.
  • Aiwei Wang
    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.
  • Xiaobo Yang
    School of Public Health, Guangxi Medical University, Nanning, Guangxi, China.
  • Chengpei Zhu
    Department of General Surgery Center, Beijing Youan Hospital, Clinical Center for Liver Cancer, Capital Medical University, Beijing, China [email protected] [email protected] [email protected] [email protected] [email protected].
  • Hanping Wang
    BNLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China.
  • Wei Sun
    Sutra Medical Inc, Lake Forest, CA.
  • Haitao Zhao
    Automation Department, School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, PR China; College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, PR China.

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