Mutational landscape of gray zone lymphoma
Bibliographic record
Abstract
The mutational landscape of gray zone lymphoma (GZL) has not yet been established, and differences from related entities are largely unknown. Here, we studied coding sequence mutations of 50 Epstein-Barr virus (EBV)-negative GZLs and 20 polymorphic EBV+ diffuse large B-cell lymphoma (DLBCL) not otherwise specified (poly-EBV-L) in comparison with classical Hodgkin lymphoma (cHL), primary mediastinal large B-cell lymphoma (PMBCL), and DLBCL. Exomes of 21 GZL and 7 poly-EBV-L cases, along with paired constitutional DNA, were analyzed as a discovery cohort, followed by targeted sequencing of 217 genes in an extension cohort of 29 GZL and 13 poly-EBV-L cases. GZL cases with thymic niche involvement (anterior mediastinal mass) exhibited a mutation profile closely resembling cHL and PMBCL, with SOCS1 (45%), B2M (45%), TNFAIP3 (35%), GNA13 (35%), LRRN3 (32%), and NFKBIA (29%) being the most recurrently mutated genes. In contrast, GZL cases without thymic niche involvement (n = 18) had a significantly distinct pattern that was enriched in mutations related to apoptosis defects (TP53 [39%], BCL2 [28%], BIRC6 [22%]) and depleted in GNA13, XPO1, or NF-κB signaling pathway mutations (TNFAIP3, NFKBIE, IKBKB, NFKBIA). They also exhibited more BCL2/BCL6 rearrangements compared with thymic GZL. Poly-EBV-L cases presented a distinct mutational profile, including STAT3 mutations and a significantly lower coding mutation load in comparison with EBV- GZL. Our study highlights characteristic mutational patterns in GZL associated with presentation in the thymic niche, suggesting a common cell of origin and disease evolution overlapping with related anterior mediastinal lymphomas.
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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) | 0.000 | 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) | 0.000 | 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".