RECAP-seq: restriction enzyme-based CpG-methylated fragment amplification for early cancer detection
Bibliographic record
Abstract
Aberrant DNA methylation drives cancer development, yet current screening methods require substantial resources for targeted enrichment across multiple CpG-rich regions. Early cancer detection in cell-free DNA (cfDNA) presents additional challenges due to low circulating tumor DNA fractions (0.01-10%) that dilute cancer-specific signals. To address these limitations, we developed Restriction Enzyme-based CpG-methylated fragment AmPlification sequencing (RECAP-seq) to selectively enrich hypermethylated fragments from existing Enzymatic Methyl-seq (EM-seq) libraries. RECAP-seq combines EM-seq library preparation with BstUI restriction enzyme digestion to target CGCG motifs, achieving preferential enrichment of CpG islands. With spike-in experiments using cell line mixtures, RECAP-seq successfully distinguished samples as low as 0.001%. The method identified 7,091 hypermethylated markers, including ALX4 which showed progressive increases with colorectal cancer stage. Clinical validation using cfDNA from 35 healthy donors and 47 colorectal cancer patients demonstrated robust detection with an area under the curve (AUC) of 0.932, achieving 78.7% sensitivity at 95% specificity.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".