The intersection of genetic and chemical genomic screens identifies GSK-3α as a target in human acute myeloid leukemia
Bibliographic record
Abstract
Acute myeloid leukemia (AML) is the most common form of acute leukemia in adults. Long-term survival of patients with AML has changed little over the past decade, necessitating the identification and validation of new AML targets. Integration of genomic approaches with small-molecule and genetically based high-throughput screening holds the promise of improved discovery of candidate targets for cancer therapy. Here, we identified a role for glycogen synthase kinase 3α (GSK-3α) in AML by performing 2 independent small-molecule library screens and an shRNA screen for perturbations that induced a differentiation expression signature in AML cells. GSK-3 is a serine-threonine kinase involved in diverse cellular processes, including differentiation, signal transduction, cell cycle regulation, and proliferation. We demonstrated that specific loss of GSK-3α induced differentiation in AML by multiple measurements, including induction of gene expression signatures, morphological changes, and cell surface markers consistent with myeloid maturation. GSK-3α-specific suppression also led to impaired growth and proliferation in vitro, induction of apoptosis, loss of colony formation in methylcellulose, and anti-AML activity in vivo. Although the role of GSK-3β has been well studied in cancer development, these studies support a role for GSK-3α in AML.
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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) | 0.000 | 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".