The Mixed Lineage Leukemia (MLL) Fusion–Associated Gene <i>AF4</i> Promotes CD133 Transcription
Bibliographic record
Abstract
The AC133 epitope has been used as a marker for both normal and cancer stem cells from multiple tissue lineages. To identify transcription factors that regulate CD133 expression, we conducted parallel large-scale RNA interference screens in Caco-2 cancer cells that endogenously express CD133 and in engineered HEK293 cells that express CD133 from a heterologous promoter. The transcription factor AF4 was identified following a comparative analysis between the two screens. We then showed that AF4 is a promoter of CD133 transcription in multiple cancer cell lines. Knockdown of AF4 resulted in a dramatic reduction in CD133 transcript levels. Importantly, a subset of pediatric acute lymphoblastic leukemias (ALL) harbor a fusion oncogene results from a chromosomal translocation that juxtaposes the mixed-lineage leukemia (MLL) gene and the AF4 gene. An investigation of the functional role of CD133 in the MLL-AF4-dependent ALL cells revealed that CD133 was required for leukemia cell survival. Together, our findings show AF4-dependent regulation of CD133 expression, which is required for the growth of ALL cells. CD133 may therefore represent a therapeutic target in a subset of cancers.
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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 | 0.001 |
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".