Genetic Subgroups Inform on Pathobiology in Adult and Pediatric Burkitt Lymphoma
Bibliographic record
Abstract
Abstract Burkitt lymphoma (BL) accounts for the majority of pediatric non-Hodgkin lymphomas (NHL) and is relatively rare but significantly more lethal when diagnosed in adults. The global incidence is highest in Sub-Saharan Africa, where Epstein-Barr virus (EBV) positivity is observed in 95% of all tumors. Both pediatric (pBL) and adult (aBL) cases are known to share some driver mutations, for example MYC translocations, which are seen in > 90% of cases. Sequencing efforts have identified many common somatic alterations that cooperate with MYC in lymphomagenesis with approximately 30 significantly mutated genes (SMG) reported thus far. Recent analyses revealed non-coding mutation patterns in pBL that were attributed to aberrant somatic hypermutation (aSHM). We sought to identify genomic and molecular features that may explain clinical disparities within and between aBL and pBL in an effort to delineate BL subtypes that may allow for the stratification of patients with shared pathobiology. Through comprehensive sequencing of BL genomes, we found additional SMGs, including more genetic features that associate with tumor EBV status, and established three new genetic subgroups that span pBL and aBL. Direct comparisons between pBL and aBL revealed only marginal differences and the mutational profiles were consistently better explained by EBV status. Using an unsupervised clustering approach to identify subgroupings within BL and diffuse large B-cell lymphoma (DLBCL), we have defined three genetic subgroups that predominantly comprise BL tumors. Akin to the recently defined DLBCL subgroups, each BL subgroup is characterized by combinations of common driver mutations and non-coding mutations caused by aSHM. Two of these subgroups and their prototypical genetic features ( ID3 and TP53 ) had significant associations with patient outcomes that were different among the aBL and pBL cohorts. These findings highlight not only a shared pathogenesis between aBL and pBL, but also establish genetic subtypes within BL that serve to delineate tumors with distinct molecular features, providing a new framework for epidemiological studies, and diagnostic and therapeutic strategies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".