A transgenic mouse model demonstrating the oncogenic role of mutations in the polycomb-group gene EZH2 in lymphomagenesis
Bibliographic record
Abstract
The histone methyltransferase EZH2 is frequently mutated in germinal center-derived diffuse large B-cell lymphoma and follicular lymphoma. To further characterize these EZH2 mutations in lymphomagenesis, we generated a mouse line where EZH2(Y641F) is expressed from a lymphocyte-specific promoter. Spleen cells isolated from the transgenic mice displayed a global increase in trimethylated H3K27, but the mice did not show an increased tendency to develop lymphoma. As EZH2 mutations often coincide with other mutations in lymphoma, we combined the expression of EZH2(Y641F) by crossing these transgenic mice with Eµ-Myc transgenic mice. We observed a dramatic acceleration of lymphoma development in this combination model of Myc and EZH2(Y641F). The lymphomas show histologic features of high-grade disease with a shift toward a more mature B-cell phenotype, increased cycling and gene expression, and epigenetic changes involving important pathways in B-cell regulation and function. Furthermore, they initiate disease in secondary recipients. In summary, EZH2(Y641F) can collaborate with Myc to accelerate lymphomagenesis demonstrating a cooperative role of EZH2 mutations in oncogenesis. This murine lymphoma model provides a new tool to study global changes in the epigenome caused by this frequent mutation and a promising model system for testing novel treatments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".