Comprehensive comparison of enzymatic and bisulfite DNA methylation analysis in clinically relevant samples
Bibliographic record
Abstract
BACKGROUND: Bisulfite conversion is considered the gold standard for DNA methylation analysis, but it damages DNA and performs sub-optimally with clinical samples (e.g., formalin-fixed paraffin-embedded and circulating free plasma DNA (cfDNA)). Here we describe a comprehensive comparison of bisulfite and enzymatic methylation sequencing, using commercially available assays in clinically relevant patient samples and cell lines. We also report the first clinical enzymatic whole genome methylation sequencing (WGMS) in a cohort of patients with chronic lymphocytic leukemia (CLL). We report data from a multi-arm experiment comprising controlled reference material and clinically relevant samples to assess technical differences between enzymatic and chemical methylation conversion technologies. RESULTS: Enzymatic methylation sequencing was highly concordant to bisulfite data but outperformed bisulfite conversion in key sequencing metrics; the enzymatic method demonstrated significantly higher estimated counts of unique reads, reduced DNA fragmentation, and higher library yields than bisulfite conversion. Enzymatic conversion produced inferior methylation array data. Although bisulfite and enzymatic methods were highly concordant, the increased quality of multiple sequencing metrics seen in the enzymatic method enabled the development of robust clinical sample pipelines including targeted sequencing in cfDNA. CONCLUSIONS: Using the enzymatic methylation sequencing methods described, we report a putative link of interleukin (IL)-15 methylation changes to acalabrutinib treatment response in a CLL clinical trial cohort (ACE-CL-001 trial, NCT02029443).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".