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

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.