Metastatic Hormone-Sensitive Prostate Cancer
Conditions
Keywords
Metastatic hormone-sensitive prostate cancer (mHSPC), Spatial multi-omics, ADT combined with androgen receptor pathway inhibitors (ADT+ARPI), Treatment response prediction model, Precision medicine / individualized therapy
Brief summary
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.
Detailed description
1、Baseline sample collection: Tumor tissues will be collected via prostate biopsy from enrolled patients. The specimens will be tripartite: one aliquot for standard histopathology, one for WES, and one for spatial multi-omic profiling (Xenium and PhenoCycler-Fusion).2、Follow-up: Patients will be assessed every 3 months during therapy, with CBC, biochemistry, sex hormones, and serum PSA. Prostate mpMRI will be repeated every 3 months. PSA testing frequency may be modified if PSA progression occurs. Follow-up continues until CRPC development or death.3、（1）Primary: Build a prognostic model integrating Xenium, WES, and PCF data.（2）Secondary: bPFS and OS.（3）Progression: PSA rise to ≥0.2 ng/mL confirmed on repeat testing after prior undetectable levels.
Interventions
DRUGADT plus abiraterone or other ARPIs（apalutamide, enzalutamide, rezvilutamide, darolutamide)
The 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
Sponsors
Anhui Medical University
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
TREATMENT
Masking
NONE
Intervention model description
* Patient recruitment and enrollment (targeting approximately 40 patients). * Pre-treatment sample collection and spatial multi-omics profiling. * Patients receiving ADT combined with ARPI therapy. ④ Regular follow-up and collection of efficacy data. ⑤ Integration of spatial multi-omics data with clinical efficacy data. ⑥ Screening for spatial molecular biomarkers predictive of treatment response. ⑦ Construction and validation of a predictive efficacy model.
Eligibility
Sex/Gender
MALE
Age
18 Years to 85 Years
Healthy volunteers
No
Inclusion criteria
1. Age \> 18 years and \< 85 years. 2. Histopathologically confirmed prostate adenocarcinoma, ductal adenocarcinoma, or intraductal carcinoma. 3. Imaging evidence of definite distant metastases (according to RECIST criteria). 4. Pre-biopsy PSA ≥ 20 ng/mL or Gleason score ≥ 4+4. 5. No prior hormonal therapy or other systemic anti-tumor regimens. 6. ECOG performance status 0-2, with an estimated life expectancy \> 6 months. 7. Adequate organ function.h. Ability and willingness to provide written informed consent, and capability to comply with the study visit schedule.
Exclusion criteria
1. Histopathological diagnosis of neuroendocrine or small cell prostate cancer. 2. No definite distant metastases detected on imaging. 3. Prior history of anti-tumor therapy (including neoadjuvant, adjuvant, or other treatments). 4. Submitted biopsy samples fail to meet quality control requirements. 5. Concurrent severe endocrine or metabolic disorders, or other severe digestive system diseases. 6. Concurrent chronic hepatitis, cirrhosis, chronic nephritis, renal insufficiency, or other relevant conditions. 7. History of immunodeficiency or organ transplantation. 8. History of other concurrent malignancies. 9. Concurrent enrollment in other clinical trials. 10. Other conditions that the investigator deems unsuitable for study enrollment.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Biochemical Progression-Free Survival (bPFS) | From treatment initiation until biochemical progression or last follow-up, assessed every 3 months (±1 month) for up to 24 months. | 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) | From treatment initiation until death or last follow-up, assessed up to 24 months. | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Mutation Frequency of Key Genes Assessed by Whole-Exome Sequencing (WES) | Baseline (at enrollment, from biopsy tissue). | 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 (at enrollment). | 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) | Baseline (at enrollment). | 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 data used to predict outcome at 6 months after treatment initiation. | 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. |
Countries
China
Contacts
CONTACTSheng Tai, M.D.
CONTACTHongzhi Wang, M.D.
PRINCIPAL_INVESTIGATORSheng Tai, M.D.
The First Affiliated Hospital of Anhui Medical University
PRINCIPAL_INVESTIGATORHongzhi Wang, M.D.
The First Affiliated Hospital of Anhui Medical University
Outcome results
None listed