The interaction between Myc and Miz1 is required to antagonize TGFβ-dependent autocrine signaling during lymphoma formation and maintenance
Bibliographic record
Abstract
The Myc protein suppresses the transcription of several cyclin-dependent kinase inhibitors (CKIs) via binding to Miz1; whether this interaction is important for Myc's ability to induce or maintain tumorigenesis is not known. Here we show that the oncogenic potential of a point mutant of Myc (MycV394D) that is selectively deficient in binding to Miz1 is greatly attenuated. Binding of Myc to Miz1 is continuously required to repress CKI expression and inhibit accumulation of trimethylated histone H3 at Lys 9 (H3K9triMe), a hallmark of cellular senescence, in T-cell lymphomas. Lymphomas that arise express high amounts of transforming growth factor beta-2 (TGFbeta-2) and TGFbeta-3. Upon Myc suppression, TGFbeta signaling is required to induce CKI expression and cellular senescence and suppress tumor recurrence. Binding of Myc to Miz1 is required to antagonize growth suppression and induction of senescence by TGFbeta. We demonstrate that, since lymphomas express high levels of TGFbeta, they are poised to elicit an autocrine program of senescence upon Myc inactivation, demonstrating that TGFbeta is a key factor that establishes oncogene addiction of T-cell lymphomas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".