Transcriptional Heterogeneity Overcomes Super-Enhancer Disrupting Drug Combinations in Multiple Myeloma
Why this work is in the frame
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Bibliographic record
Abstract
Multiple myeloma (MM) is a malignancy that is often driven by MYC and that is sustained by IRF4, which are upregulated by super-enhancers. IKZF1 and IKZF3 bind to super-enhancers and can be degraded using immunomodulatory imide drugs (IMiD). Successful IMiD responses downregulate MYC and IRF4; however, this fails in IMiD-resistant cells. MYC and IRF4 downregulation can also be achieved in IMiD-resistant tumors using inhibitors of BET and EP300 transcriptional coactivator proteins; however, in vivo these drugs have a narrow therapeutic window. By combining IMiDs with EP300 inhibition, we demonstrate greater downregulation of MYC and IRF4, synergistic killing of myeloma in vitro and in vivo, and an increased therapeutic window. Interestingly, this potent combination failed where MYC and IRF4 expression was maintained by high levels of the AP-1 factor BATF. Our results identify an effective drug combination and a previously unrecognized mechanism of IMiD resistance. SIGNIFICANCE: These results highlight the dependence of MM on IKZF1-bound super-enhancers, which can be effectively targeted by a potent therapeutic combination pairing IMiD-mediated degradation of IKZF1 and IKZF3 with EP300 inhibition. They also identify AP-1 factors as an unrecognized mechanism of IMiD resistance in MM. See related article by Neri, Barwick, et al., p. 56. See related commentary by Yun and Cleveland, p. 5. This article is featured in Selected Articles from This Issue, p. 4.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it