Sensitive detection of tumor nucleic acids in plasma by mutation-enriched next generation sequencing.
Bibliographic record
Abstract
e22041 Background: Next Generation DNA Sequencing (NGS) is becoming the new standard for mutational profiling of tumour tissue, due to its flexibility, speed, and decreasing cost. While generally exceptional in performance, NGS suffers from a sequencing error rate of 0.1% – 1%, largely due to amplification-induced artifacts in its workflow. While this does not constitute a significant problem in application of NGS to sequencing of tumour tissue, it makes NGS impractical as a method to search for low abundance mutation signatures in plasma samples. Numerous publications have shown the presence of tumour signatures in the cell-free DNA (cfDNA) circulating in plasma, but concordance between the tumour signature and the plasma signature has been limited. This is likely due to limitations in the detection technologies used to search for cfDNA in plasma. To maximize concordance between plasma and tissue, it will be essential that sensitivities reaching 0.01% and below (as little as a single tumour mutant allele per sample) be achieved, and ideally that multiple mutational hot spots be analysed to maximize the chance of detection. Current technologies are incapable of such sensitivity over a large number of mutation loci. Methods: We have developed a novel electrophoretic method that can enrich nucleic acid samples over 1,000,000-fold for up to 100 somatic mutations, enabling reliable profiling of samples containing as little as 0.01% mutant. By enriching nucleic acid samples for specific targets prior to amplification and sequencing, we enable the use of NGS in plasma-based mutation detection and profiling. Results: We present technical and clinical data demonstrating highly sensitive multiplexed mutation detection in plasma and tissue samples, demonstrating 0.01% sensitivity over 45 somatic mutations per sample. Conclusions: We have demonstrated a novel somatic mutation enrichment methodology that allows DNA sequencing to work beyond its usual limit of detection to accurately profile solid tumours by detecting their mutation signature in plasma, even when the tumour DNA is present in plasma at abundances below 0.01%.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".