A syntenin-deficient microenvironment educates AML for aggressiveness
Bibliographic record
Abstract
Abstract In acute myeloid leukemia (AML), the stromal microenvironment plays a prominent role in promoting tumor cell survival and progression. Although widely explored, the crosstalk between leukemic and stromal cells remains poorly understood. Syntenin, a multi-domain PDZ protein, controls both the trafficking and signaling of key molecules involved in intercellular communication. Therefore, we aimed to clarify the role of environmental syntenin in the progression of AML. By in vivo approaches in syngeneic mice, we demonstrate that a syntenin-deficient environment reprograms AML blasts to survive independently of the stroma. Up-regulation of EEF1A2 in the blasts controls this gain of cell survival. Furthermore, using ex vivo co-culture systems, we show that syntenin-deficient bone marrow stromal cells (BMSC) enhance the survival of different types of AML cells, including patient samples, and suffice to educate syngeneic AML, recapitulating micro-environmental effects observed in vivo. We establish that syntenin-deficiency causes an increase of eIF5A and autophagy-related factors in BMSC, and provide evidence that the inhibition of autophagy prevents syntenin-deficient BMSC to stimulate AML survival. Altogether, these findings indicate that host-syntenin in the BM microenvironment acts as a repressor of AML aggressiveness. Key points - A syntenin-deficient host reprograms AML blasts, enhancing total protein synthesis and cell survival pathways - Autophagy in the syntenin-deficient microenvironment is responsible for the gain of AML cell survival
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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".