Influence of SLAM Family Receptors in the NK‐Cell‐Mediated Surveillance of Lymphoblastic Acute Leukemia
Bibliographic record
Abstract
Acute lymphoblastic leukemia (ALL) is the most common pediatric cancer worldwide. Along with other lymphocytes, NK cells have important functions in the recognition and elimination of malignant cells, and thus are of interest for the development of novel therapeutic treatments of various cancers, including leukemia. The signaling lymphocyte activation molecule (SLAM) family includes surface molecules that are expressed in hematopoietic cells, where they regulate distinct cell responses. Altered expression and/or function of these receptors may be involved in the etiology of several diseases. Here we report the altered overall expression of SLAM family receptors (SFR) in leukemia cells from pediatric patients. Additionally, we found that the expression of a single type of SLAM receptor in leukemia target cells was sufficient to upregulate the release of cytotoxic granules from primary NK cells. Moreover, coating the leukemia cell surface with specific engagers containing recombinant SFR was sufficient to enhance NK-cell degranulation and unleash the cytotoxic competence of primary NK cells from ALL patients. Finally, the NK effector responses promoted by SLAM receptor engagement were dependent on the ability to recruit PLC-γ. Overall, these findings suggest that SFR are a promising resource for the treatment of pediatric acute lymphoblastic leukemia.
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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) | 0.001 | 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".