Identification of Druggable Targets for MEF2D Fusion Proteins in Acute Lymphoblastic Leukemia (ALL)
Bibliographic record
Abstract
MEF2D-rearranged acute lymphoblastic leukemia (ALL) is a high-risk subtype associated with poor prognosis, frequent relapse, and resistance to standard therapies. MEF2D fusion proteins, such as MEF2D::BCL9 and MEF2D::HNRNPUL1, drive leukemia by rewiring transcriptional programs, but transcription factors are difficult to target directly. To identify potential therapeutic targets, I used BioID proximity labeling in HEK293 Flp-In T-REx cells to systematically map proteins interacting with these fusions and compared them to wild-type MEF2D. This approach identified 186 and 209 high-confidence interactors for MEF2D::BCL9 and MEF2D::HNRNPUL1, respectively, including 53 shared, fusion-specific proteins. Gene ontology analysis revealed enrichment in RNA metabolism, RNA stability, post-transcriptional silencing, and transcriptional regulation, highlighting multilayered rewiring of cellular networks. Notably, histone deacetylases (HDACs), particularly HDAC9, emerged as direct, high-confidence interactors, suggesting they help silence tumor-suppressive genes. These findings provide new insights into MEF2D fusion biology and highlight HDACs as promising therapeutic targets in this aggressive leukemia subtype.
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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 | 0.000 |
| 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".