An <i>ERG</i> Enhancer–Based Reporter Identifies Leukemia Cells with Elevated Leukemogenic Potential Driven by ERG-USP9X Feed-Forward Regulation
Bibliographic record
Abstract
Abstract Acute leukemia is a rapidly progressing blood cancer with low survival rates. Unfavorable prognosis is attributed to insufficiently characterized subpopulations of leukemia stem cells (LSC) that drive chemoresistance and leukemia relapse. Here we utilized a genetic reporter that assesses stemness to enrich and functionally characterize LSCs. We observed heterogeneous activity of the ERG+85 enhancer–based fluorescent reporter in human leukemias. Cells with high reporter activity (tagBFPHigh) exhibited elevated expression of stemness and chemoresistance genes and demonstrated increased clonogenicity and resistance to chemo- and radiotherapy as compared with their tagBFPNeg counterparts. The tagBFPHigh fraction was capable of regenerating the original cellular heterogeneity and demonstrated increased invasive ability. Moreover, the tagBFPHigh fraction was enriched for leukemia-initiating cells in a xenograft assay. We identified the ubiquitin hydrolase USP9X as a novel ERG transcriptional target that sustains ERG+85–positive cells by controlling ERG ubiquitination. Therapeutic targeting of USP9X led to preferential inhibition of the ERG-dependent leukemias. Collectively, these results characterize human leukemia cell functional heterogeneity and suggest that targeting ERG via USP9X inhibition may be a potential treatment strategy in patients with leukemia. Significance: This study couples a novel experimental tool with state-of-the-art approaches to delineate molecular mechanisms underlying stem cell-related characteristics in leukemia cells.
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How this classification was reachedexpand
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 | 0.001 | 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 teacher head, 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".