Targeting STAT5 Signaling Overcomes Resistance to IDH Inhibitors in Acute Myeloid Leukemia through Suppression of Stemness
Bibliographic record
Abstract
Mutant isocitrate dehydrogenase 1 (IDH1) and IDH2 block the differentiation of acute myeloid leukemia (AML) cells through production of R-2-hydroxyglutarate (R-2-HG). IDH inhibitors can induce differentiation of AML cells by lowering R-2-HG but have limited clinical efficacy as single agents. Here, we performed a genome-wide CRISPR knockout screen in an Idh1-mutated hematopoietic progenitor cell line to identify genes that increased the differentiation response to ivosidenib, an IDH1 inhibitor. The screen identified C-type lectin member 5a (Clec5a), which encodes a spleen tyrosine kinase (SYK)-coupled surface receptor, as one of the top hits. Knockout of Clec5a and Syk rendered cells more sensitive to ivosidenib-induced differentiation through a reduction in STAT5-dependent expression of stemness-related genes, including genes in the homeobox (HOX) family. Importantly, direct inhibition of STAT5 activity was sufficient to increase the differentiation response to IDH inhibitors in primary human IDH1- and IDH2-mutated AML cells, including those harboring mutations in receptor tyrosine kinase (RTK) and MAPK genes that have been linked to drug resistance. In patient-derived xenograft models of IDH1-mutated AML, combination treatment with ivosidenib and the STAT5 inhibitor pimozide was superior to each agent alone in inducing differentiation in leukemic cells without compromising normal hematopoiesis. These findings demonstrate that STAT5 is a critical mediator of resistance to IDH inhibitors and provide the rationale for combining STAT5 and IDH inhibitors in the treatment of IDH-mutated AML. SIGNIFICANCE: A CRISPR knockout screen identifies a mechanism of resistance to IDH inhibitors in AML involving activated STAT5 signaling, suggesting a potential strategy to improve the clinical efficacy of IDH inhibitors.
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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) | 0.001 | 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".