Inhibition of autophagy as a treatment strategy for p53 wild-type acute myeloid leukemia
Bibliographic record
Abstract
Abstract Here we have explored whether inhibition of autophagy can be used as a treatment strategy for acute myeloid leukemia (AML). Steady-state autophagy was measured in leukemic cell lines and primary human CD34+ AML cells with a large variability in basal autophagy between AMLs observed. The autophagy flux was higher in AMLs classified as poor risk, which are frequently associated with TP53 mutations (TP53mut), compared with favorable- and intermediate-risk AMLs. In addition, the higher flux was associated with a higher expression level of several autophagy genes, but was not affected by alterations in p53 expression by knocking down p53 or overexpression of wild-type p53 or p53R273H. AML CD34+ cells were more sensitive to the autophagy inhibitor hydroxychloroquine (HCQ) than normal bone marrow CD34+ cells. Similar, inhibition of autophagy by knockdown of ATG5 or ATG7 triggered apoptosis, which coincided with increased expression of p53. In contrast to wild-type p53 AML (TP53wt), HCQ treatment did not trigger a BAX and PUMA-dependent apoptotic response in AMLs harboring TP53mut. To further characterize autophagy in the leukemic stem cell-enriched cell fraction AML CD34+ cells were separated into ROSlow and ROShigh subfractions. The immature AML CD34+-enriched ROSlow cells maintained higher basal autophagy and showed reduced survival upon HCQ treatment compared with ROShigh cells. Finally, knockdown of ATG5 inhibits in vivo maintenance of AML CD34+ cells in NSG mice. These results indicate that targeting autophagy might provide new therapeutic options for treatment of AML since it affects the immature AML subfraction.
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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".