DNA methylation signatures define molecular subtypes of diffuse large B-cell lymphoma
Bibliographic record
Abstract
Expression profiling has shown 2 main and clinically distinct subtypes of diffuse large B-cell lymphomas (DLBCLs): germinal-center B cell-like (GCB) and activated B cell-like (ABC) DLBCLs. Further work has shown that these subtypes are partially characterized by distinct genetic alterations and different survival. Here, we show with the use of an assay that measures DNA methylation levels of 50,000 CpG motifs distributed among more than 14,000 promoters that these 2 DLBCL subtypes are also characterized by distinct epigenetic profiles. DNA methylation and gene expression profiling were performed on a cohort of 69 patients with DLBCL. After assigning ABC or GCB labels with a Bayesian expression classifier trained on an independent dataset, a supervised analysis identified 311 differentially methylated probe sets (263 unique genes) between ABC and GCB DLBCLs. Integrated analysis of methylation and gene expression showed a core tumor necrosis factor-α signaling pathway as the principal differentially perturbed gene network. Sixteen genes overlapped between the core ABC/GCB methylation and expression signatures and encoded important proteins such as IKZF1. This reduced gene set was an accurate predictor of ABC and GCB subtypes. Collectively, the data suggest that epigenetic patterning contributes to the ABC and GCB DLBCL phenotypes and could serve as useful biomarker.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
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 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".