Drug-mediated inhibition of Fli-1 for the treatment of leukemia
Bibliographic record
Abstract
The Ets transcription factor, Fli-1 is activated in murine erythroleukemia and overexpressed in various human malignancies including Ewing's sarcoma, induced by the oncogenic fusion protein EWS/Fli-1. Recent studies by our group and others have demonstrated that Fli-1 plays a key role in tumorigenesis, and disrupting its oncogenic function may serve as a potential treatment option for malignancies associated with its overexpression. Herein, we describe the discovery of 30 anti-Fli-1 compounds, characterized into six functional groups. Treatment of murine and human leukemic cell lines with select compounds inhibits Fli-1 protein or mRNA expression, resulting in proliferation arrest and apoptosis. This anti-cancer effect was mediated, at least in part through direct inhibition of Fli-1 function, as anti-Fli-1 drug treatment inhibited Fli-1 DNA binding to target genes, such as SHIP-1 and gata-1, governing hematopoietic differentiation and proliferation. Furthermore, treatment with select Fli-1 inhibitors revealed a positive relationship between the loss of DNA-binding activity and Fli-1 phosphorylation. Accordingly, anti-Fli-1 drug treatment significantly inhibited leukemogenesis in a murine erythroleukemia model overexpressing Fli-1. This study demonstrates the ability of this drug-screening strategy to isolate effective anti-Fli-1 inhibitors and highlights their potential use for the treatment of malignancies overexpressing this oncogene.
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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 | 0.000 |
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".