ETV4-Dependent Transcriptional Plasticity Maintains <i>MYC</i> Expression and Results in IMiD Resistance in Multiple Myeloma
Bibliographic record
Abstract
Immunomodulatory drugs (IMiD) are a backbone therapy for multiple myeloma (MM). Despite their efficacy, most patients develop resistance, and the mechanisms are not fully defined. Here, we show that IMiD responses are directed by IMiD-dependent degradation of IKZF1 and IKZF3 that bind to enhancers necessary to sustain the expression of MYC and other myeloma oncogenes. IMiD treatment universally depleted chromatin-bound IKZF1, but eviction of P300 and BRD4 coactivators only occurred in IMiD-sensitive cells. IKZF1-bound enhancers overlapped other transcription factor binding motifs, including ETV4. Chromatin immunoprecipitation sequencing showed that ETV4 bound to the same enhancers as IKZF1, and ETV4 CRISPR/Cas9-mediated ablation resulted in sensitization of IMiD-resistant MM. ETV4 expression is associated with IMiD resistance in cell lines, poor prognosis in patients, and is upregulated at relapse. These data indicate that ETV4 alleviates IKZF1 and IKZF3 dependency in MM by maintaining oncogenic enhancer activity and identify transcriptional plasticity as a previously unrecognized mechanism of IMiD resistance. SIGNIFICANCE: We show that IKZF1-bound enhancers are critical for IMiD efficacy and that the factor ETV4 can bind the same enhancers and substitute for IKZF1 and mediate IMiD resistance by maintaining MYC and other oncogenes. These data implicate transcription factor redundancy as a previously unrecognized mode of IMiD resistance in MM. See related article by Welsh, Barwick, et al., p. 34. 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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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".