Abstract 5025: Extracellular vesicle DNA as a potential biomarker for cancer detection: A comparative analysis with ctDNA
Bibliographic record
Abstract
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.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".