Presence of Urinary Exosomes for Liquid Biopsy of Clear Cell Renal Cell Carcinoma: Protocol for a Pilot Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Approximately 70%-80% of kidney cancers are clear cell renal cell carcinomas (CCRCCs). Patient management is based on imaging (abdominal ultrasound and computerized tomography), surgical excision of the tumor, and pathological analysis. A tissue biopsy is therefore necessary to confirm the diagnosis and avoid unnecessary nephrectomy. For metastatic cancers, a tissue biopsy is essential for establishing the targeted therapy. This biopsy of tumor material is invasive and painful. Other techniques such as liquid biopsy would help reduce the need for tissue biopsy. The development of a simple biological test for diagnosis is essential. CA9 is a powerful marker for the diagnosis of CCRCC. Exosomes have become a major source of liquid biopsy because they carry tumor proteins, RNA, and lipids. Urine is the most convenient biological liquid for exosome sampling. OBJECTIVE: The aim of this study (PEP-C study) is mainly to determine whether it is possible to detect urinary exosomal CA9 for the molecular diagnosis of CCRCC. METHODS: This study will include 60 patients with CCRCC and 40 noncancer patients. Exosomes will be isolated from urine samples and exosomal CA9 will be detected by transmission electron microscopy, flow cytometry, and reverse transcription-quantitative polymerase chain reaction. RESULTS: This study is currently underway with funding support from the CHU Saint-Etienne of France. CONCLUSIONS: We expect to demonstrate that urinary tumor exosomes could be a novel liquid biopsy to diagnose CCRCC and to guide clinicians in treatment decision-making. TRIAL REGISTRATION: ClinicalTrials.gov NCT04053855; https://clinicaltrials.gov/ct2/show/NCT04053855. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/24423.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".