A Study of Pre-Analytical Variables and Optimization of Extraction Method for Circulating Tumor DNA Measurements by Digital Droplet PCR
Bibliographic record
Abstract
BACKGROUND: Circulating free DNA (cfDNA) is an exciting novel method to diagnose, monitor, and predict resistance and response to cancer therapies, with the potential to radically alter the management of cancer patients. To fulfill its potential, greater knowledge about preanalytical variables is required to optimize and standardize the collection process, and maximize the yield and utility of the small quantities of cfDNA extracted. METHODS: To this end, we have compared the cfDNA extraction efficiency of three different protocols, including a protocol developed in house (Jewish General Hospital). We evaluated the impact on cfDNA levels of preanalytical variables including speed and timing of the second centrifugation and the use of k-EDTA and CTAD blood collection tubes. Finally, we analyzed the impact on fractional abundance of targeted pre-amplification and whole genome amplification on tumor and circulating tumor DNA (ctDNA) from patients with breast cancer. RESULTS: Making use of a novel protocol for cfDNA extraction we increased cfDNA quantities, up to double that of commercial kits. We found that a second centrifugation at 3,000 g on frozen plasma is as efficient as a high-speed (16,000 g) centrifugation on fresh plasma and does not affect cfDNA levels. CONCLUSIONS: These results allow for the implementation of protocols more suitable to the clinical setting. Finally, we found that, unlike targeted gene amplification, whole genome amplification resulted in altered fractional abundance of selected ctDNA variants. IMPACT: Our study of the preanalytical variables affecting cfDNA recovery and testing will significantly enhance the quality and application of ctDNA testing in clinical oncology.
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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 | 0.001 | 0.001 |
| 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".