Autophagy limits proliferation and glycolytic metabolism in acute myeloid leukemia
Bibliographic record
Abstract
Abstract Decreased autophagy contributes to malignancies; however, it is unclear how autophagy has an impact on tumor growth. Acute myeloid leukemia (AML) is an ideal model to address this as (i) patient samples are easily accessible, (ii) the hematopoietic stem and progenitor cells (HSPC) where transformation occurs is well characterized and (iii) loss of the key autophagy gene Atg7 in HSPCs leads to a lethal pre-leukemic phenotype in mice. Here we demonstrate that loss of Atg5 results in an identical HSPC phenotype as loss of Atg7, confirming a general role for autophagy in HSPC regulation. Compared with more committed/mature hematopoietic cells, healthy human and mouse HSPCs displayed enhanced basal autophagic flux, limiting mitochondrial damage and reactive oxygen species in this long-lived population. Taken together, with our previous findings these data are compatible with autophagy-limiting leukemic transformation. In line with this, autophagy gene losses are found within chromosomal regions that are commonly deleted in human AML. Moreover, human AML blasts showed reduced expression of autophagy genes and displayed decreased autophagic flux with accumulation of unhealthy mitochondria, indicating that deficient autophagy may be beneficial to human AML. Crucially, heterozygous loss of autophagy in an MLL–ENL model of AML led to increased proliferation in vitro, a glycolytic shift and more aggressive leukemias in vivo. With autophagy gene losses also identified in multiple other malignancies, these findings point to low autophagy, providing a general advantage for tumor growth.
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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".