CXCR4 inhibitors selectively eliminate CXCR4-expressing human acute myeloid leukemia cells in NOG mouse model
Bibliographic record
Abstract
The chemokine receptor CXCR4 favors the interaction of acute myeloid leukemia (AML) cells with their niche but the extent to which it participates in pathogenesis is unclear. Here, we show that CXCR4 expression at the surface of leukemic cells allowed distinguishing CXCR4 (high) from CXCR4(neg/low) AML patients. When high levels of CXCR4 are expressed at the surface of AML cells, blocking the receptor function with small molecule inhibitors could promote leukemic cell death and reduce NOD/Shi-scid/IL-2Rγ(null) (NOG) leukemia-initiating cells (LICs). Conversely, these drugs had no efficacy when AML cells do not express CXCR4 or when they do not respond to chemokine CXC motif ligand 12 (CXCL12). Functional analysis showed a greater mobilization of leukemic cells and LICs in response to drugs, suggesting that they target the interaction between leukemic cells and their supportive bone marrow microenvironment. In addition, increased apoptosis of leukemic cells in vitro and in vivo was observed. CXCR4 expression level on AML blast cells and their migratory response to CXCL12 are therefore predictive of the response to the inhibitors and could be used as biomarkers to select patients that could potentially benefit from the drugs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".