AICDA drives epigenetic heterogeneity and accelerates germinal center-derived lymphomagenesis
Bibliographic record
Abstract
Epigenetic heterogeneity is emerging as a feature of tumors. In diffuse large B-cell lymphoma (DLBCL), increased cytosine methylation heterogeneity is associated with poor clinical outcome, yet the underlying mechanisms remain unclear. Activation-induced cytidine deaminase (AICDA), an enzyme that mediates affinity maturation and facilitates DNA demethylation in germinal center (GC) B cells, is required for DLBCL pathogenesis and linked to inferior outcome. Here we show that AICDA overexpression causes more aggressive disease in BCL2-driven murine lymphomas. This phenotype is associated with increased cytosine methylation heterogeneity, but not with increased AICDA-mediated somatic mutation burden. Reciprocally, the cytosine methylation heterogeneity characteristic of normal GC B cells is lost upon AICDA depletion. These observations are relevant to human patients, since DLBCLs with high AICDA expression manifest increased methylation heterogeneity vs. AICDA-low DLBCLs. Our results identify AICDA as a driver of epigenetic heterogeneity in B-cell lymphomas with potential significance for other tumors with aberrant expression of cytidine deaminases.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".