Current Outlook on Autophagy in Human Leukemia: Foe in Cancer Stem Cells and Drug Resistance, Friend in New Therapeutic Interventions
Bibliographic record
Abstract
Autophagy is an evolutionarily conserved cellular recycling process in cell homeostasis and stress adaptation. It confers protection and promotes survival in response to metabolic/environmental stress, and is upregulated in response to nutrient deprivation, hypoxia, and chemotherapies. Autophagy is also known to sustain malignant cell growth and contributes to cancer stem cell survival when challenged by cytotoxic and/or targeted therapies, a potential mechanism of disease persistence and drug resistance that has gathered momentum. However, different types of human leukemia utilize autophagy in complex, context-specific manners, and the molecular and cellular mechanisms underlying this process involve multiple protein networks that will be discussed in this review. There is mounting preclinical evidence that targeting autophagy can enhance the efficacy of cancer therapies. Chloroquine and other lysosomal inhibitors have spurred initiation of clinical trials and demonstrated that inhibition of autophagy restores chemosensitivity of anticancer drugs, but with limited autophagy-dependent effects. Intriguingly, several autophagy-specific inhibitors, with better therapeutic indexes and lower toxicity, have been developed. Promising preclinical studies with novel combination approaches as well as potential challenges to effectively eradicate drug-resistant cells, particularly cancer stem cells, in human leukemia are also detailed in this review.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".