A Role for E2F Activities in Determining the Fate of Myc-Induced Lymphomagenesis
Bibliographic record
Abstract
The phenotypic heterogeneity that characterizes human cancers reflects the enormous genetic complexity of the oncogenic process. This complexity can also be seen in mouse models where it is frequently observed that in addition to the initiating genetic alteration, the resulting tumor harbors additional, somatically acquired mutations that affect the tumor phenotype. To investigate the role of genetic interactions in the development of tumors, we have made use of the Emu-myc model of pre-B and B cell lymphoma. Since various studies point to a functional interaction between Myc and the Rb/E2F pathway, we have investigated the role of E2F activities in the process of Myc-induced lymphomagenesis. Whereas the absence of E2F1 and E2F3 function has no impact on Myc-mediated tumor development, the absence of E2F2 substantially accelerates the time of tumor onset. Conversely, tumor development is delayed by the absence of E2F4. The enhanced early onset of tumors seen in the absence of E2F2 coincides with an expansion of immature B lineage cells that are likely to be the target for Myc oncogenesis. In contrast, the absence of E2F4 mutes the response of the lineage to Myc and there is no expansion of immature B lineage cells. We also find that distinct types of tumors emerge from the Emu-myc mice, distinguished by different patterns of gene expression, and that the relative proportions of these tumor types are affected by the absence of either E2F2 or E2F4. From these results, we conclude that there are several populations of tumors that arise from the Emu-myc model, reflecting distinct populations of cells that are susceptible to Myc-mediated oncogenesis and that the proportion of these cell populations is affected by the presence or absence of E2F activities.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".