IMiDs Synergize with EP300 Inhibition to Disrupt the Ikzf/MYC/IRF4 Axis in Multiple Myeloma
Bibliographic record
Abstract
Multiple myeloma (MM) is a heterogeneous plasma cell malignancy for which current therapies eventually fail. In myeloma, endogenous and translocated superenhancers drive the expression of the lineage-defining transcription factor IRF4 and oncogene MYC, respectively - both considered "undruggable". Successful standard-of-care immunomodulatory imide drugs (IMiDs) degrade superenhancer-binding pioneer factors IKAROS and AIOLOS; however, depletion of IKAROS and AIOLOS does not always correlate with a successful IMiD response, and patients treated with IMiDs ultimately relapse. We show responses to the IMiD Pomalidomide (POM) are directly correlated with downregulation of IRF4 and MYC. In IMiD-resistant cells and tumors, IRF4 and MYC downregulation could be achieved with novel coactivator-targeting drugs including the BET inhibitor JQ1 and the CBP/EP300 inhibitor GNE-781, but these were toxic and lacked a therapeutic window in vivo. We show how POM combined with GNE781 synergize and downregulate MYC and IRF4 levels, killing myeloma cells in vitro and in vivo. However, resistance to this combination was induced by expression of the AP-1 factor BATF, which directly maintained IRF4 expression and myeloma cell survival. These results identify a potent therapeutic combination and a hereto unrealized mechanism of IMiD resistance in multiple myeloma.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".