Stress Granules Underlie Acute Myeloid Leukemia Stem Cell Survival and Stress Adaptation
Bibliographic record
Abstract
ABSTRACT The link between cancer maintenance and an ability to sustain continued growth through stresses conferred by the cancer state itself is growing. However, there are significant gaps in our understanding of how this stress is managed, particularly at the level of cancer initiating cells. Here, we identify proteins comprising the dynamic, stress-adaptive ribonucleoprotein complexes known as stress granules (SG) to be enriched among the factors essential for leukemic stem cell (LSC)-driven leukemic propagation. Focusing on core SG nucleator G3BP1, we dissect the role of SGs in human acute myeloid leukemia (AML), their targetability, and the mechanisms they govern to uncover a novel propensity for AML, and in particular LSC-enriched fractions, to prime the expression of SG components, form SGs with greater fidelity and to be reliant on their establishment and continued integrity for LSC maintenance. We further unveil the transcript and protein interactome of G3BP1 in the AML context and show that consolidated control of innate immune signaling, and apoptosis repression is executed through regional binding specificity of G3BP1 to highly structured 3’UTRs and cooperation with the RNA helicase UPF1 to mediate transcript decay in SGs. Altogether our findings advance novel fundamental principles of stress adaptation exploited in AML and LSCs that may extend to other cancers and uncover SGs as a novel axis for therapy development.
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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) | 0.002 | 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".