Activating Signals Dominate Inhibitory Signals in CD137L/IL-15 Activated Natural Killer Cells
Bibliographic record
Abstract
Natural killer (NK) cells can mediate potent antitumor effects, but factors regulating the efficiency of tumor lysis remain unclear. Studies in allogeneic stem cell transplantation highlight an important role for killer cell immunoglobulin-like receptor (KIR) mismatch in overcoming human leukocyte antigen-mediated inhibitory signals. However, other activating and inhibitory signals also modulate tumor lysis by NK cells. We used rhIL15 and artificial antigen presenting cells expressing CD137L and IL15Rα to activate and expand peripheral blood NK cells (CD137L/IL15 NK) up to 1000-fold in 3 weeks. Compared with resting NK cells, CD137L/IL15 NK cells show modest increases in KIR expression and substantial increases in NKG2D, tumor necrosis factor-related apoptosis-inducing ligand, and natural cytotoxicity receptors (NCRs: NKp30, NKp44, NKp46). Compared with resting NK cells, CD137L/IL15 NK cells mediate enhanced cytotoxicity against allogeneic and autologous tumors and KIR signaling did not substantially inhibit cytotoxicity. Rather, tumor lysis by CD137L/IL15 activated NK cells was predominantly driven by NCR signaling as blockade of NCRs dramatically diminished the lysis of a wide array of tumor targets. Furthermore, tumor lysis by CD137L/IL15 NK cells was tightly linked to NCR expression levels that peaked on day 8 to 10 after NK activation, and cytotoxicity diminished on subsequent days as NCR expression declined. We conclude that KIR mismatch is not a prerequisite for tumor killing by CD137L/IL15 NK cells and that NCR expression provides a biomarker for predicting potency of CD137L/IL15 NK cells in studies of NK cell-based immunotherapy.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".