Plasma Exosomal RNA Signature for Prostate Cancer Bone Metastasis (EXO-MET)

Plasma Exosomal RNA Signature for Predicting PSMA PET-Defined Bone Metastasis in Prostate Cancer: A Prospective, Multicenter Discovery, Development, and Validation Study

Brief Summary:

This prospective, multicenter study aims to discover, develop, and validate a plasma exosomal RNA-based signature as a rule-out test for predicting bone metastasis in prostate cancer, using baseline treatment-naïve PSMA PET as the gold standard. The study is designed in four sequential phases:

Phase 1 (Discovery, n=250): High-throughput sequencing of plasma exosomal RNAs to identify differentially expressed candidate RNAs.

Phase 2 (Model Development, n=300): Digital droplet PCR (ddPCR) analysis of candidates in an independent cohort to construct and lock the final multi-RNA predictive signature using appropriate machine learning methods.

Phase 3 (Internal Validation, n=300): Independent validation of the locked signature in a consecutive cohort reflecting natural disease prevalence.

Phase 4 (External Validation, n=150): Final independent validation in a multi-center cohort enriched for bone metastasis.

Primary Outcome:

To evaluate the diagnostic performance of the signature as a rule-out test for PSMA PET-defined bone metastasis. The primary performance metrics are:

Sensitivity, with a prespecified target of ≥95% (to ensure minimal false negatives).

Specificity at the threshold that achieves the ≥95% sensitivity. A specificity of ≥30% will be considered supportive of clinical utility. A specificity of ≥30% (or a lower bound of the 95% confidence interval exceeding 20%) will be considered supportive of clinical utility.

Need:

Current biomarkers lack sensitivity and specificity for early detection of bone metastasis. More importantly, existing tools lack adequate negative predictive value to safely rule out bone metastasis in low-risk patients, leading to over-imaging or delayed detection. There is an urgent need for a non-invasive rule-out test to safely defer PSMA PET/CT in very-low-risk patients. Plasma exosomal RNAs offer a promising liquid biopsy approach, but prospective multicenter studies with rigorous validation are lacking.

Secondary Outcomes:

1. Secondary metrics include negative predictive value (NPV), positive predictive value (PPV), area under the ROC curve (AUC), calibration, and decision curve analysis.
2. Correlation between exosomal RNA levels and number of bone metastatic lesions (PSMA PET).
3. Association with PSA, PSMA PET SUVmax, and MRI findings.
4. Tissue-plasma correlation to confirm tumor origin (exploratory).
5. Mechanistic exploration of key candidates via in vitro/in vivo assays (exploratory).
6. Subgroup analyses by hormone sensitivity, metastatic pattern, Gleason grade (exploratory).

Inclusion Criteria:

1. Histologically confirmed prostate cancer scheduled for baseline PSMA PET.
2. PSMA PET performed prior to any prostate cancer-related treatment.
3. Blood samples collected prior to any treatment AND prior to prostate biopsy.
4. Willing to undergo prostate biopsy if clinically indicated (after blood collection).
5. Written informed consent.
6. Age ≥18 years.

Exclusion Criteria:

1. Any prior prostate cancer treatment before baseline PSMA PET.
2. Blood samples collected after prostate biopsy.
3. Other active malignancy within past two years (excluding non-melanoma skin cancer).
4. Inadequate blood sample quality or quantity.
5. Severe comorbidities interfering with study conduct.

Study Overview

• Status: Recruiting
• Conditions: Liquid Biopsy; Bone Metastasis; Prostate Cancer (Diagnosis); PSMA PET; Exosomal RNA; Biomarker Discovery and Validation; Treament-naive

Detailed Description

Background Prostate cancer (PCa) is a leading cause of cancer-related morbidity worldwide, with bone metastasis being the most frequent and devastating complication. Early and accurate detection of bone metastasis is critical for timely intervention and improved patient outcomes. However, current biomarkers such as PSA lack sufficient sensitivity and specificity for early metastasis detection, and conventional imaging (e.g., bone scan, CT) often identifies metastases only after they become clinically apparent. PSMA PET has emerged as the most sensitive imaging modality for defining metastatic status at baseline, but its high cost, limited availability, and radiation exposure preclude its use as a universal screening tool.

Concurrently, liquid biopsy-particularly the analysis of plasma exosomal RNAs-offers a unique window into tumor biology. Exosomal RNAs are stable in circulation, reflect the molecular characteristics of the primary tumor, and can be detected using sensitive methods such as high-throughput sequencing and digital droplet PCR. Despite their promise, large-scale prospective multicenter studies with rigorous multi-phase validation are lacking.

Need There is a critical unmet need for a non-invasive, robust biomarker that can identify patients at very low risk of bone metastasis, thereby allowing safe deferral of PSMA PET/CT. Existing tools lack adequate negative predictive value to confidently rule out bone metastasis in low-risk populations. A successful rule-out test would reduce unnecessary imaging, lower healthcare costs, and minimize patient radiation exposure. This study implements a four-stage design with phase-specific sample size considerations aligned with contemporary standards for biomarker development.

Study Design and Technical Phases This is a prospective, multicenter, phase-sequential biomarker development and validation study. The gold standard for bone metastasis status is baseline, treatment-naïve PSMA PET/CT. All blood samples are collected prior to any prostate cancer-related treatment and prior to prostate biopsy (if performed) to avoid biopsy-induced contamination. Whole blood (approximately 10 mL in EDTA tubes) is processed within 2 hours to obtain plasma, which is stored at -80°C until analysis.

The four phases are defined as:

Phase 1 (Discovery, n=250): High-throughput RNA sequencing (e.g., small RNA-seq) of plasma exosomes. Differential expression analysis (e.g., DESeq2 or edgeR) identifies candidate RNAs distinguishing patients with versus without bone metastasis. Multiple testing correction (FDR <0.05) is applied.

Phase 2 (Model Development, n=300): An independent cohort enriched for bone metastasis (approximately 200 positive, 100 negative) is used for quantitative measurement of candidate RNAs using ddPCR. A continuous risk-scoring model is constructed using machine learning (e.g., regularized regression, random forests, or gradient boosting). The model is locked after internal cross-validation, before any validation data are examined.

Phase 3 (Internal Validation, n=300): A consecutive cohort reflecting natural disease prevalence (expected bone metastasis rate ~30%) is used to evaluate the locked model. In this phase, a single cut-off value is selected to achieve a sensitivity of ≥95%. The specificity at that cut-off is the primary endpoint. Secondary metrics (NPV, PPV, AUC, calibration, decision curve analysis) are also assessed.

Phase 4 (External Validation, n=150): A geographically distinct, multi-center cohort enriched for bone metastasis is used to assess generalizability, applying the same cut-off determined in Phase 3.

Statistical Considerations Sample Size Justification Sample sizes were chosen based on published recommendations for phased biomarker studies and on formal precision analysis for the primary endpoint (specificity at the ≥95% sensitivity threshold).

Phase 1 (Discovery, n=250): This sample size is typical for high-throughput discovery studies. With an expected bone metastasis prevalence of ~30%, approximately 75 positive and 175 negative cases will be available, providing sufficient power to detect differential expression with FDR <0.05.

Phase 2 (Model Development, n=300): Following the "events per variable" (EPV) rule (≥10 events per candidate predictor) and assuming a parsimonious final model (≤20 candidate RNAs after Phase 1), a minimum of 200 positive events is required. The cohort is enriched for bone metastasis (target ~67% positive), thus a total of 300 patients (≈200 positive, ≈100 negative) is planned.

Phase 3 (Internal Validation, n=300): The primary endpoint is specificity at the cut-off achieving ≥95% sensitivity. Assuming a true specificity of 35% at this threshold, a sample of approximately 210 negative patients (from total n=300 with ~30% bone metastasis prevalence) yields a 95% confidence interval half-width of approximately ±6-7%. This precision is more than sufficient to rule out a clinically useless specificity (e.g., <15%) and allows robust subgroup analyses.

Phase 4 (External Validation, n=150): This multi-center cohort is enriched for bone metastasis (target ~40-50% positive). The sample size of 150 (≈75 positive, ≈75 negative) allows precise estimation of specificity (95% CI half-width ±11% assuming 35% specificity) and sensitivity in an independent setting, confirming generalizability.

All sample sizes may be adjusted modestly based on actual recruitment; any adjustments will be documented.

Statistical Analysis Plan (High-Level) Analyses will be performed using R (version ≥4.2). The final analysis plan will be finalized before locking validation data.

Phase 1: Differential expression analysis (DESeq2/edgeR). Candidate selection based on fold change, adjusted p-value, and abundance.

Phase 2: Machine learning model building using cross-validation. The final continuous model is locked.

Phase 3: Apply the locked model to the internal validation cohort. Determine the cut-off value that achieves sensitivity ≥95%. Report specificity, NPV, PPV, AUC, calibration, and decision curve analysis at this cut-off.

Phase 4: Apply the same cut-off to the external validation cohort and repeat the performance evaluation.

Secondary/exploratory analyses: Correlation, subgroup analyses, mechanistic studies (as detailed in the secondary outcome measures).

Data Management Data will be stored centrally in a REDCap database with 3-step authentication. Data entry will occur approximately every 3-6 months. Patient confidentiality will be maintained.

• Study Type: Observational
• Enrollment (Estimated): 1000

Contacts and Locations

• Study Contact: Name: Jianhua Jiao, MD.; Phone Number: +86 18700919857; Email: 1531769428@qq.com

Study Locations

• China
  • Gansu
    • Lanzhou, Gansu, China
      • Recruiting
      • The First Hospital of Lanzhou University
      • Principal Investigator: Wei Zhang, MD.
      • Contact: Wei Zhang, MD.; Phone Number: +86 18700919857; Email: 1531769428@qq.com
  • Ningxia
    • Yinchuan, Ningxia, China
      • Recruiting
      • General Hospital of Ningxia Medical University
      • Contact: Zhiyong Lv, MD.; Phone Number: +86 18700919857; Email: 1531769428@qq.com
      • Principal Investigator: Zhiyong Lv, MD.
  • Shaanxi
    • Weinan, Shaanxi, China
      • Recruiting
      • Weinan Central Hospital
      • Contact: Weihong Zhao, MD.; Phone Number: +86 18700919857; Email: 1531769428@qq.com
      • Principal Investigator: Weihong Zhao, MD.
    • Xi'an, Shaanxi, China, 710032
      • Recruiting
      • Xijing Hospital
      • Contact: Jianhua Jiao, MD.; Phone Number: +86 18700919857; Email: 1531769428@qq.com
      • Principal Investigator: Weijun Qin, MD.
    • Xi'an, Shaanxi, China
      • Recruiting
      • Shaanxi provincial people's hospital
      • Contact: Yi Sun, MD.; Phone Number: +86 18700919857; Email: 1531769428@qq.com
      • Principal Investigator: Yi Sun, MD.
    • Xi'an, Shaanxi, China
      • Recruiting
      • Xijing 986 Hospital
      • Contact: Wuhe Zhang, MD.; Phone Number: +86 18700919857; Email: 1531769428@qq.com
      • Principal Investigator: Wuhe Zhang, MD.
    • Xianyang, Shaanxi, China
      • Recruiting
      • The Second Affiliated Hospital of Shaanxi University of Chinese Medicine
      • Contact: Wei Zheng, MD.; Phone Number: +86 18700919857; Email: 1531769428@qq.com
      • Principal Investigator: Wei Zheng, MD.
    • Xining, Shaanxi, China
      • Recruiting
      • Qinghai University Affiliated Hospital
      • Contact: Guojun Chen, MD.; Phone Number: +86 18700919857; Email: 1531769428@qq.com
      • Principal Investigator: Guojun Chen, MD.
    • Yan’an, Shaanxi, China
      • Recruiting
      • Affiliated Hospital of Yan'an University
      • Contact: Jixue Gao, MD.; Phone Number: +86 18700919857; Email: 1531769428@qq.com
      • Principal Investigator: Gao, MD.

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

The study enrolls a total of approximately 1000 patients with histologically confirmed prostate cancer from multiple centers. All patients undergo baseline PSMA PET/CT and provide pre-treatment blood samples (collected before any therapy and before prostate biopsy). The single overall cohort is allocated into four sequential, independent phases: Phase 1 (Discovery, n=250), Phase 2 (Model Development, n≥300, bone-metastasis enriched), Phase 3 (Internal Validation, n≥300, natural prevalence), and Phase 4 (External Validation, n=150, multi-center enriched). Bone metastasis status is defined by PSMA PET/CT.

Description

Inclusion Criteria

1. Patients with histologically confirmed prostate cancer who are scheduled to undergo baseline PSMA PET imaging.
2. Patients who undergo PSMA PET imaging prior to any prostate cancer-related treatment (including androgen deprivation therapy, radiotherapy, or surgery).

3. Patients who provide blood samples for plasma exosomal RNA analysis collected prior to any treatment AND prior to prostate biopsy (if applicable).

   Whole blood samples (approximately 10 mL) will be collected in EDTA tubes at this specified time point. Samples will be processed within 2 hours to obtain plasma and stored at -80°C until analysis.This timing ensures circulating exosomal RNA profiles reflect tumor biology without biopsy-induced contamination.

4. Patients who are willing to undergo prostate biopsy if clinically indicated (biopsy performed after blood collection).
5. Patients who provide written informed consent to participate in the study.
6. Age ≥18 years.

Exclusion Criteria

1. Patients who have received any prior prostate cancer-related treatment before the baseline PSMA PET scan (including hormonal therapy, radiotherapy, chemotherapy, or surgery).
2. Patients whose blood samples were collected after prostate biopsy.
3. Patients with a history of other active malignancies within the past two years (excluding non-melanoma skin cancer).
4. Patients with inadequate blood sample quality or quantity for exosomal RNA extraction and analysis (e.g., hemolysis, insufficient volume <8 mL).
5. Patients with severe comorbidities or conditions that, in the judgment of the investigator, could interfere with study compliance or pose significant risk.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Prostate Cancer Cohort; A single cohort of 1000 patients with suspected or histologically confirmed prostate cancer who undergo baseline treatment-naïve PSMA PET imaging. All patients provide blood samples for plasma exosomal RNA analysis, collected prior to any treatment and prior to prostate biopsy. This cohort is used for a four-phase biomarker study: Phase 1 (Discovery, n=250) for RNA sequencing to identify candidate biomarkers; Phase 2 (Model Development, n=300) for digital PCR-based signature development; Phase 3 (Internal Validation, n=300) for independent validation in a consecutive cohort; and Phase 4 (External Validation, n=150) for multi-center validation. Bone metastasis status is defined by PSMA PET. Phase 2 and Phase 3 cohorts are temporally and geographically independent. No patient is included in more than one phase

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Specificity of the plasma exosomal RNA-based predictive signature for detecting PSMA PET-defined bone metastasis at a prespecified sensitivity threshold of ≥95%	Description: The signature will be developed as a continuous risk score using machine learning in an independent development cohort (Phase 2, n≥300, enriched for bone metastasis). After locking the model, a single cut-off value will be selected in the internal validation cohort (Phase 3, n≥300, reflecting natural disease prevalence) to achieve a sensitivity of ≥95% for detecting PSMA PET/CT-defined bone metastasis. The primary outcome is the specificity of the signature at that cut-off. A specificity of ≥30% (or a lower bound of the 95% confidence interval exceeding 20%) will be considered supportive of clinical utility. Measurement tools and units: Plasma exosomal RNA level: digital droplet PCR (ddPCR), expressed as absolute copy number per mL of plasma. Bone metastasis status: PSMA PET/CT, binary (positive/negative). Sensitivity and specificity: proportions with 95% confidence intervals (Clopper-Pearson exact method).	Baseline

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Correlation between plasma exosomal RNA level and bone metastatic lesion count on PSMA PET	The number of bone metastatic lesions will be quantified by PSMA PET/CT as a discrete count. The Spearman rank correlation coefficient (rho) will be calculated to assess the monotonic association with exosomal RNA level.	Baseline
Correlation between plasma exosomal RNA level and serum PSA level	Serum PSA will be measured in ng/mL using standard clinical laboratory methods. Spearman correlation coefficient will be calculated.	Baseline
Correlation between plasma exosomal RNA level and PSMA PET SUVmax	SUVmax (maximum standardized uptake value) will be derived from PSMA PET/CT. Spearman correlation coefficient will be calculated.	Baseline
Association between exo-RNA and MRI findings (exploratory)	MRI findings will be categorized asPI-RADs scores, "suspicious for bone metastasis" or "not suspicious".	Baseline
Correlation between tissue RNA expression (RNA-seq, normalized counts like FPKM/TPM) and plasma exosomal RNA level (ddPCR, copies/mL)	In patients with available formalin-fixed paraffin-embedded (FFPE) or fresh frozen tumor tissue samples, RNA expression of the selected exosomal RNA candidates (e.g., the top 2-3 most differentially expressed RNAs from the locked signature) will be measured by RNA sequencing (RNA-seq). Expression levels will be quantified as normalized counts (e.g., fragments per kilobase of transcript per million mapped reads [FPKM] or transcripts per million [TPM]) using standard bioinformatics pipelines (e.g., STAR + featureCounts + DESeq2 normalization). The corresponding plasma exosomal RNA level of the same candidate is measured by digital droplet PCR (ddPCR) and expressed as absolute copy number per mL of plasma. The Spearman rank correlation coefficient (rho) and its 95% confidence interval will be calculated to assess the strength of association between tissue and plasma levels.	Baseline
Mechanistic exploration of key driver candidates via functional assays (exploratory)	For the most significantly dysregulated RNA candidate(s) from the validated signature, functional assays will be performed: Cell proliferation: CCK-8 assay measured as absorbance at 450 nm, or EdU assay measured as percentage of EdU-positive cells. Cell migration/invasion: Transwell assay measured as number of migrated/invaded cells per high-power field (average of 5 fields). In vivo tumor growth: tumor volume measured by caliper in mm³ using formula (length × width²)/2. In vivo metastasis: number of macroscopic metastatic nodules counted. Descriptive statistics (mean ± SD, median with IQR) will be reported.	Baseline
Subgroup analyses of diagnostic performance (exploratory)	The locked signature's performance (sensitivity, specificity, negative predictive value, positive predictive value) will be calculated separately for: Hormone-sensitive vs. castration-resistant prostate cancer Oligometastatic (≤3 lesions) vs. polymetastatic (>3 lesions) disease Gleason grade group ≤7 vs. ≥8 Each performance metric will be reported with 95% confidence intervals.	Baseline

Collaborators and Investigators

• Sponsor: Xijing Hospital
• Collaborators: LanZhou University; Qinghai University; Shaanxi Provincial People's Hospital; Air Force Military Medical University, China; General Hospital of Ningxia Medical University; Weinan Central Hospital; Yan'an University Affiliated Hospital; The Second Affiliated Hospital of Shaanxi University of Chinese Medicine

Study record dates

Study Major Dates

• Study Start (Actual): March 12, 2026
• Primary Completion (Estimated): June 30, 2027
• Study Completion (Estimated): December 31, 2027

Study Registration Dates

• First Submitted: March 11, 2026
• First Submitted That Met QC Criteria: April 20, 2026
• First Posted (Actual): April 28, 2026

Study Record Updates

• Last Update Posted (Actual): April 28, 2026
• Last Update Submitted That Met QC Criteria: April 20, 2026
• Last Verified: March 1, 2026

More Information

Terms related to this study

• Keywords: Prostate Cancer; Liquid Biopsy; Bone Metastasis; PSMA PET; Exosomal RNA; Predictive Signature
• Additional Relevant MeSH Terms: Urogenital Diseases; Genital Diseases; Pathologic Processes; Genital Neoplasms, Male; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Genital Diseases, Male; Prostatic Diseases; Male Urogenital Diseases; Pathological Conditions, Signs and Symptoms; Prostatic Neoplasms; Disease
• Other Study ID Numbers: KY20262011-F-1

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No