This study plans to enroll patients with newly diagnosed metastatic hormone-sensitive prostate cancer (mHSPC) and conduct a prospective, single-center, observational study. By performing whole-exome sequencing (WES), Xenium spatial transcriptomics, and PhenoCycler-Fusion spatial single-cell proteomics (PCF analysis) on tumor tissue samples, we aim to comprehensively delineate the molecular landscape of patients with different spatial multi-omic profiles in the real-world setting. We will investigate the associations between these molecular features and differential treatment responses to various therapeutic regimens, and further construct predictive models of treatment response. Ultimately, this will enable precise evaluation of treatment outcomes across distinct molecular subtypes and provide evidence to support individualized precision diagnostics and therapeutics for patients with mHSPC.
Age > 18 years and < 85 years.
Histopathologically confirmed prostate adenocarcinoma, ductal adenocarcinoma, or intraductal carcinoma.
Imaging evidence of definite distant metastases (according to RECIST criteria).
Pre-biopsy PSA ≥ 20 ng/mL or Gleason score ≥ 4+4.
Histopathological diagnosis of neuroendocrine or small cell prostate cancer.
No definite distant metastases detected on imaging.
Prior history of anti-tumor therapy (including neoadjuvant, adjuvant, or other treatments).
Submitted biopsy samples fail to meet quality control requirements.
Single group
ADT plus abiraterone or other ARPIs（apalutamide, enzalutamide, rezvilutamide, darolutamide)
DrugThe spatial heterogeneity of the tumor microenvironment in mHSPC-encompassing immune cell infiltration patterns, tumor-stroma interface features, and the regional activation status of critical signaling pathways-is intimately linked to clinical outcomes with ADT plus ARPI therapy. Through comprehensive spatial multi-omic profiling, these spatial attributes can be systematically dissected to uncover pivotal predictive biomarkers, facilitate the development of accurate response prediction models, and ultimately guide personalized therapeutic strategies for patients with mHSPC
Biochemical Progression-Free Survival (bPFS)
Time from initiation of ADT plus ARPI therapy to biochemical progression or death from any cause, whichever occurs first. Biochemical progression is defined as a PSA rise to ≥0.2 ng/mL after having reached an undetectable level, confirmed by a second measurement at least 2 weeks apart. Participants without an event will be censored at the date of last follow-up.
Overall Survival (OS)
Time from treatment initiation to death from any cause. Participants alive or lost to follow-up will be censored at the date last known alive.
Mutation Frequency of Key Genes Assessed by Whole-Exome Sequencing (WES)
Baseline tumor tissue from prostate biopsy will be analyzed by WES. The mutation status (including variant allele frequency) of AR, TP53, PTEN, RB1, and other relevant genes will be reported as the proportion of participants with each mutation.
Spatial Gene Expression Signatures Measured by Xenium Platform
Baseline biopsy tissue will be processed for Xenium in situ spatial transcriptomics. The average expression levels of a pre-specified gene panel (including AR-signaling and immune-related genes) in tumor, immune, and stromal compartments will be reported.
Spatial Protein Marker Expression Measured by PhenoCycler-Fusion (PCF)
Using cyclic immunofluorescence on baseline biopsy tissue, the platform quantifies the density (cells/mm²) and proportion of positive cells for a panel of protein markers (e.g., AR, PSMA, PD-L1, CD8, CD68) within the tumor microenvironment.
Area Under the Receiver Operating Characteristic Curve (AUC) of the Multi-omics Prediction Model for 6-Month Undetectable PSA（ PSA <0.2 ng/mL ）
Baseline WES, Xenium, and PCF data will be integrated using a machine-learning algorithm (e.g., random forest or LASSO-Cox) to build a model predicting Undetectable PSA at 6 months ( defined as serum PSA \<0.2 ng/mL confirmed at two consecutive visits). Model performance will be evaluated by cross-validation, and the mean AUC with 95% confidence interval will be reported, along with sensitivity, specificity, and positive predictive value.
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