Small Extracellular Vesicles as Biomarkers in Sarcoma Follow-Up: Protocol for a Prospective, Multicentric Pilot Study
Bibliographic record
Abstract
BACKGROUND: Sarcomas are rare cancer with a heterogeneous group of tumors. They affect both genders across all age groups and present significant heterogeneity, with more than 70 histological subtypes. Despite tailored treatments, the high metastatic potential of sarcomas remains a major factor in poor patient survival, as metastasis is often the leading cause of death. Currently, metastatic risk assessment relies mainly on histological grading; yet, this method has limitations due to the disease's heterogeneity. Advances in genomic and transcriptomic research have identified potential molecular signatures, but these approaches lack reproducibility and prognostic reliability. Therefore, new biomarkers are essential for improving risk prediction and therapy adaptation. Recent studies highlight that sarcoma cells secrete extracellular vesicles, particularly small extracellular vesicles (sEVs). These nanovesicles, abundant in bodily fluids such as blood, urine, and saliva, play a crucial role in tumor development, growth, and metastasis. sEVs contain proteins and nucleic acids that mirror tumor characteristics. Given their presence in blood, sEVs offer a promising avenue for noninvasive molecular cancer analysis via liquid biopsy. Preliminary studies in Ewing sarcoma have shown substantial alterations in sEV-derived transcripts, underscoring their potential in tracking disease progression and treatment efficacy. OBJECTIVE: This study aims to investigate whether sEVs can serve as reliable biomarkers for monitoring sarcoma progression and predicting recurrence risk. METHODS: This prospective, multicentric pilot study will enroll adult patients diagnosed with localized or metastatic liposarcomas, leiomyosarcomas, or undifferentiated pleomorphic sarcomas at 3 French cancer centers. The study's primary goal is to quantify sEVs and analyze their protein and RNA content in the blood of patients with localized or metastatic sarcomas before and after the treatment. sEVs will be isolated from plasma samples, and protein and microRNA concentration will be determined. Research will last, on average, 6 months for patients with localized sarcoma and 4 months for patients with metastatic sarcoma. RESULTS: We expect to identify differences in exosome levels based on disease stage and observe correlations between exosome dynamics and treatment response. If confirmed, these findings could establish sEVs as noninvasive biomarkers for monitoring therapy effectiveness and disease progression in patients with sarcoma. CONCLUSIONS: This study could establish a novel, noninvasive biomarker for sarcoma prognosis and treatment monitoring. If successful, a nationwide study will be launched to confirm findings in a larger patient cohort, potentially revolutionizing sarcoma management and improving patient outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT03800121; https://clinicaltrials.gov/ct2/show/study/NCT03800121. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/63718.
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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 | 0.025 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".