An Isoform-Specific RUNX1C–BTG2 Axis Governs AML Quiescence and Chemoresistance
Bibliographic record
Abstract
Aberrant levels or structures of RNA isoforms are a hallmark of many cancers, including acute myeloid leukemia (AML), yet their role in AML chemoresistance remains unclear. We conducted a paired analysis of RNA isoform changes in patients with AML before therapy and at relapse after chemotherapy and identified intragenic DNA methylation at the proximal promoter of the transcription factor RUNX1, which resulted in elevated expression of the long-isoform RUNX1C through its alternative distal promoter. The unique N-terminal region of RUNX1C orchestrated an isoform-specific transcriptional program that promoted chemoresistance, with its direct target BTG2 playing a role in chemotherapy resistance. BTG2 promoted rRNA deadenylation, resulting in decreased mRNA expression and stability. Deletion of rRNAs increased cellular quiescence. Moreover, RNA-based targeting of RUNX1C reactivated quiescent leukemia cells and enhanced chemotherapy efficacy. These findings delineated an isoform-specific transcriptional circuit that governed chemotherapy response, providing a potential therapeutic strategy to mitigate AML recurrence. SIGNIFICANCE: This study identifies RUNX1C as a contributor to AML chemoresistance and an inducer of quiescence through BTG2. Targeting RUNX1C with RNA-based approaches disrupts this state and improves chemotherapy response, highlighting RUNX1C inhibition as a promising strategy to overcome resistance and enhance treatment efficacy in AML.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".