Immune Cell Inhibition by SLAMF7 Is Mediated by a Mechanism Requiring Src Kinases, CD45, and SHIP-1 That Is Defective in Multiple Myeloma Cells
Bibliographic record
Abstract
Signaling lymphocytic activation molecule F7 (SLAMF7) is a receptor present on immune cells, including natural killer (NK) cells. It is also expressed on multiple myeloma (MM) cells. This led to development of an anti-SLAMF7 antibody, elotuzumab, showing efficacy against MM. SLAMF7 mediates activating or inhibitory effects in NK cells, depending on whether cells express or do not express the adaptor EAT-2. Since MM cells lack EAT-2, we elucidated the inhibitory effectors of SLAMF7 in EAT-2-negative NK cells and tested whether these effectors were triggered in MM cells. SLAMF7-mediated inhibition in NK cells lacking EAT-2 was mediated by SH2 domain-containing inositol phosphatase 1 (SHIP-1), which was recruited via tyrosine 261 of SLAMF7. Coupling of SLAMF7 to SHIP-1 required Src kinases, which phosphorylated SLAMF7. Although MM cells lack EAT-2, elotuzumab did not induce inhibitory signals in these cells. This was at least partly due to a lack of CD45, a phosphatase required for Src kinase activation. A defect in SLAMF7 function was also observed in CD45-deficient NK cells. Hence, SLAMF7-triggered inhibition is mediated by a mechanism involving Src kinases, CD45, and SHIP-1 that is defective in MM cells. This defect might explain why elotuzumab eliminates MM cells by an indirect mechanism involving the activation of NK cells.
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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 | 0.001 |
| 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".