Peptide-Affinity Precipitation of Extracellular Vesicles and Cell-Free DNA Improves Sequencing Performance for the Detection of Pathogenic Mutations in Lung Cancer Patient Plasma
Bibliographic record
Abstract
Liquid biopsy is a minimally-invasive diagnostic method that may improve access to molecular profiling for non-small cell lung cancer (NSCLC) patients. Although cell-free DNA (cf-DNA) isolation from plasma is the standard liquid biopsy method for detecting DNA mutations in cancer patients, the sensitivity can be highly variable. Vn96 is a peptide with an affinity for both extracellular vesicles (EVs) and circulating cf-DNA. In this study, we evaluated whether peptide-affinity (PA) precipitation of EVs and cf-DNA from NSCLC patient plasma improves the sensitivity of single nucleotide variants (SNVs) detection and compared observed SNVs with those reported in the matched tissue biopsy. NSCLC patient plasma was subjected to either PA precipitation or cell-free methods and total nucleic acid (TNA) was extracted; SNVs were then detected by next-generation sequencing (NGS). PA led to increased recovery of DNA as well as an improvement in NGS sequencing parameters when compared to cf-TNA. Reduced concordance with tissue was observed in PA-TNA (62%) compared to cf-TNA (81%), mainly due to identification of SNVs in PA-TNA that were not observed in tissue. EGFR mutations were detected in PA-TNA with 83% sensitivity and 100% specificity. In conclusion, PA-TNA may improve the detection limits of low-abundance alleles using NGS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".