Methylation-based droplet digital polymerase chain reaction shows high concordance with chronic lymphocytic leukemia <i>IGHV</i> somatic mutation status
Bibliographic record
Abstract
OBJECTIVE: Somatic hypermutation at immunoglobulin heavy chain variable (IGHV) genes, an established prognostic and predictive biomarker for chronic lymphocytic leukemia (CLL), is assessed by gene sequencing. We developed a single methylation-specific droplet digital polymerase chain reaction (methyl-ddPCR) to predict IGHV status in patients with CLL. METHODS: The CLL methylation array and IGHV data from the International Cancer Genome Consortium (ICGC) were used for biomarker discovery. Top-ranked candidate regions were manually screened for PCR primer and probe binding sites. A single methyl-ddPCR was evaluated on an internal cohort of CLLs with mutated (M), unmutated (U), and inconclusive IGHV results originally determined by next-generation sequencing (NGS). RESULTS: Analysis of ICGC data identified array probe cg23844018 as a candidate for the PCR. The corresponding CpG site showed high methylation levels in U-CLL and lower levels in M-CLL. On the internal cohort, a single optimal cutoff correctly classified 104 of 115 U- and M-CLLs (90.4%; area under the curve = 0.96). The PCR data correlated with some prognostic fluorescence in situ hybridization and CLL subset groupings. Limited analysis suggests that the PCR may be able to stratify some patients with CLL who have inconclusive results on IGHV NGS testing. CONCLUSIONS: The methyl-ddPCR showed high concordance with CLL IGHV status in an internal cohort.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".