<i>JUNB</i>, <i>DUSP2</i>, <i>SGK1</i>, <i>SOCS1</i> and <i>CREBBP</i> are frequently mutated in T-cell/histiocyte-rich large B-cell lymphoma
Bibliographic record
Abstract
T-cell/histiocyte-rich large B-cell lymphoma is a rare aggressive lymphoma showing histopathological overlap with nodular lymphocyte-predominant Hodgkin lymphoma. Despite differences in tumor microenvironment and clinical behavior, the tumor cells of both entities show remarkable similarities, suggesting that both lymphomas might represent a spectrum of the same disease. To address this issue, we investigated whether these entities share mutations. Ultra-deep targeted resequencing of six typical and 11 histopathological variants of nodular lymphocyte-predominant Hodgkin lymphoma, and nine cases of T-cell/histiocyte-rich large B-cell lymphoma revealed that genes recurrently mutated in nodular lymphocyte-predominant Hodgkin lymphoma are affected by mutations at similar frequencies in T-cell/histiocyte-rich large B-cell lymphoma. The most recurrently mutated genes were JUNB, DUSP2, SGK1, SOCS1 and CREBBP, which harbored mutations more frequently in T-cell/histiocyte-rich large B-cell lymphoma and the histopathological variants of nodular lymphocyte-predominant Hodgkin lymphoma than in its typical form. Mutations in JUNB, DUSP2, SGK1 and SOCS1 were highly enriched for somatic hypermutation hotspot sites, suggesting an important role of aberrant somatic hypermutation in the generation of these somatic mutations and thus in the pathogenesis of both lymphoma entities. Mutations in JUNB are generally rarely observed in malignant lymphomas and thus are relatively specific for nodular lymphocyte-predominant Hodgkin lymphoma and T-cell/histiocyte-rich large B-cell lymphoma at such high frequencies (5/17 and 5/9 cases with JUNB mutations, respectively). Taken together, the findings of the present study further support a close relationship between T-cell/histiocyte-rich large B-cell lymphoma and nodular lymphocyte-predominant Hodgkin lymphoma by showing that they share highly recurrent genetic lesions.
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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 | 0.001 | 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) | 0.000 | 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 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".