S100A9 induces differentiation of acute myeloid leukemia cells through TLR4
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
S100A8 and S100A9 are calcium-binding proteins predominantly expressed by neutrophils and monocytes and play key roles in both normal and pathological inflammation. Recently, both proteins were found to promote tumor progression through the establishment of premetastatic niches and inhibit antitumor immune responses. Although S100A8 and S100A9 have been studied in solid cancers, their functions in hematological malignancies remain poorly understood. However, S100A8 and S100A9 are highly expressed in acute myeloid leukemia (AML), and S100A8 expression has been linked to poor prognosis in AML. We identified a small subpopulation of cells expressing S100A8 and S100A9 in AML mouse models and primary human AML samples. In vitro and in vivo analyses revealed that S100A9 induces AML cell differentiation, whereas S100A8 prevents differentiation induced by S100A9 activity and maintains AML immature phenotype. Treatment with recombinant S100A9 proteins increased AML cell maturation, induced growth arrest, and prolonged survival in an AML mouse model. Interestingly, anti-S100A8 antibody treatment had effects similar to those of S100A9 therapy in vivo, suggesting that high ratios of S100A9 over S100A8 are required to induce differentiation. Our in vitro studies on the mechanisms/pathways involved in leukemic cell differentiation revealed that binding of S100A9 to Toll-like receptor 4 (TLR4) promotes activation of p38 mitogen-activated protein kinase, extracellular signal-regulated kinases 1 and 2, and Jun N-terminal kinase signaling pathways, leading to myelomonocytic and monocytic AML cell differentiation. These findings indicate that S100A8 and S100A9 are regulators of myeloid differentiation in leukemia and have therapeutic potential in myelomonocytic and monocytic AMLs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it