Isolation of salivary cell-free DNA for cancer detection
Bibliographic record
Abstract
Saliva is an emerging source of disease biomarkers, particularly for cancers of the head and neck. Although analysis of cell-free DNA (cfDNA) in saliva holds promise as a liquid biopsy for cancer detection, currently there are no standardized methodologies for the collection and isolation of saliva for the purposes of studying DNA. Here, we evaluated various saliva collection receptacles and DNA purification techniques, comparing DNA quantity, fragment size, source, and stability. Then, using our optimized techniques, we tested the ability to detect human papillomavirus (HPV) DNA- a bona fide cancer biomarker in a subset of head and neck cancers- from patient saliva samples. For saliva collection, we found that the Oragene OG-600 receptacle yielded the highest concentration of total salivary DNA as well as short fragments <300 bp corresponding to mononucleosomal cell-free DNA. Moreover, these short fragments were stabilized beyond 48 hours after collection in contrast to other saliva collection receptacles. For DNA purification from saliva, the QIAamp Circulating Nucleic Acid kit yielded the highest concentration of mononucleosome-sized DNA fragments. Freeze-thaw of saliva samples did not affect DNA yield or fragment size distribution. Salivary DNA isolated from the OG-600 receptacle was found to be composed of both single and double-stranded DNA, including mitochondrial and microbial sources. While levels of nuclear DNA were consistent over time, levels of mitochondrial and microbial DNA were more variable and increased 48 hours after collection. Finally, we found that HPV DNA was stable in OG-600 receptacles, was reliably detected within the saliva of patients with HPV-positive head and neck cancer, and was abundant among mononucleosome-sized cell-free DNA fragments. Our studies have defined optimal techniques for isolating DNA from saliva that will contribute to future applications in liquid biopsy-based cancer detection.
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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".