DNA Methylation Epitypes of Burkitt Lymphoma with Distinct Molecular and Clinical Features
Bibliographic record
Abstract
The genetic subtypes of Burkitt lymphoma have been defined, but the role of epigenetics remains to be comprehensively characterized. We searched genomic DNA from 218 patients across four continents for recurrent DNA methylation patterns and their associations with clinical and molecular features. We identified DNA methylation patterns that were not fully explained by the Epstein-Barr virus status or mutation status, leading to two epitypes described here as HypoBL and HyperBL. Each is characterized by distinct genomic and clinical features including global methylation, mutation burden, aberrant somatic hypermutation, and survival outcomes. Methylation, gene expression, and mutational differences between the epitypes support a model in which each arises from a distinct cell of origin. These results, pending validation in external cohorts, point to a refined risk assessment for patients with Burkitt lymphoma who may experience inferior outcomes. SIGNIFICANCE: Burkitt lymphoma can be divided into two epigenetic subtypes (epitypes), each carrying distinct biological, transcriptomic, genomic, and clinical features. Epitype is more strongly associated with clinical and mutational features than the Epstein-Barr virus status or genetic subtype, highlighting an important additional layer of Burkitt lymphoma pathogenesis.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".