Refined Procedure to Purify and Sequence Circulating Cell-Free DNA in Prostate Cancer
Bibliographic record
Abstract
Cell-free DNA (cfDNA), a fragmented DNA circulating in blood, is a promising biomarker for cancer diagnosis and monitoring. Standardization of cfDNA isolation to enhance the sensitivity of molecular analyses in prostate cancer (PCa) is required. Towards this goal, we optimized existing methods to obtain a high quantity and quality of cfDNA from low volumes of plasma. The protocol was applied to samples from healthy males and three patient categories: radical prostatectomy (RP), disease-free (>6 years post-RP), and metastatic castration-resistant PCa (mCRPC). The yield was significantly higher in mCRPC cases, and the size of fragments was shorter. We compared for the first time library preparation using two cfDNA inputs and low vs. high sequencing depth. Clonal events were observed irrespective of input and depth, but lower input showed more subclonal events. The clinical application of the refined protocols to cfDNA samples from an mCRPC patient showed no tumor fraction before RP, while it increased to 25% at the advanced stage. Among chromosomal changes and mutations, the androgen receptor gene amplification was detected. Altogether, this comprehensive study on improved cfDNA procedures is highly promising to enhance the quality of liquid biopsy-based research for discoveries and much-needed clinical applications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".