Abstract 5025: Extracellular vesicle DNA as a potential biomarker for cancer detection: A comparative analysis with ctDNA
Notice bibliographique
Résumé
Abstract Background: Liquid biopsy testing has been implemented in clinical practice because of recent advancements in detecting and characterizing circulating tumor DNA (ctDNA). The Food and Drug Administration (FDA) has approved three ctDNA liquid biopsy tests that detect mutations in cancers, which can aid clinicians in choosing the most effective treatments. While ctDNA has been validated for clinical applications, the same is not true for extracellular vesicle (EV)-associated DNA, which is another promising biomarker. Previously, our laboratory developed the Extracellular Vesicle-Associated DNA Database (EV-ADD) to provide validated experimental details and data extracted from peer-reviewed published literature on EV-DNA. The aim of our study was to investigate the potential clinical utility of EV-DNA in liquid biopsy for tumor detection by surveying studies on EV-ADD that compared ctDNA and EV-DNA. Methods: We searched the entire EV-ADD to selectively identify studies that compared cell-free DNA (cfDNA) or ctDNA and EV-DNA in cancer patients (inclusion criteria). The sensitivity and specificity of cancer detection, the concentrations of DNA, and the methods of detection were compared between ct/cfDNA and EV-DNA. We define sensitivity as the ability to detect mutations in ctDNA/EV-DNA from patient blood when the mutations are known. Specificity is defined as the proportion of true negatives from the sum of true negatives and false positives. A true negative case occurs when no driver mutation is detected in ctDNA/EV-DNA of a patient whose tumor does not have the mutation either. A false positive case occurs when a driver mutation is detected in ctDNA/EV-DNA of a patient whose tumor does not have the mutation. Results: Of 97 studies in the database, 20 met our inclusion criteria. From 12 studies that matched our definition of sensitivity, the sensitivity of EV-DNA and ctDNA ranged from 19% to 95% and 14% to 97%, respectively. From the same 12 studies, the specificity of EV-DNA and ctDNA ranged from 89% to 100% and 80% to 100%, respectively. Other studies that met our inclusion criteria but did not match our definition of sensitivity either calculated sensitivity differently (2 studies), calculated sensitivity the same way in pleural effusions (2 studies), in bronchoalveolar lavage fluid (2 studies), analyzed mitochondrial DNA (1 study), or did not calculate sensitivity (1 study). The concentrations of EV-DNA and ctDNA in blood ranged from 0.8 - 118.4 ng/ml and 4.0 - 236.5 ng/ml, respectively. Digital droplet PCR (ddPCR) and sequencing were the most common methods (45% and 35%, respectively) for detecting cancer mutations. Conclusions: Since EV-DNA performed comparably to ctDNA in detecting cancer DNA in the blood of patients, EV-DNA should be seriously considered as a potential biomarker and complementary tool for detecting and monitoring cancer tumors. Citation Format: Tad Wu, Thupten Tsering, Yunxi Chen, Amélie Nadeau, Julia V. Burnier. Extracellular vesicle DNA as a potential biomarker for cancer detection: A comparative analysis with ctDNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5025.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,010 | 0,005 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».