Targeting NK Cell Inhibitory Receptors for Precision Multiple Myeloma Immunotherapy
Bibliographic record
Abstract
Innate immune surveillance of cancer involves multiple types of immune cells including the innate lymphoid cells (ILCs). Natural killer (NK) cells are considered the most active ILC subset for tumor elimination because of their ability to target infected and malignant cells without prior sensitization. NK cells are equipped with an array of activating and inhibitory receptors (IRs); hence NK cell activity is controlled by balanced signals between the activating and IRs. Multiple myeloma (MM) is a hematological malignancy that is known for its altered immune landscape. Despite improvements in therapeutic options for MM, this disease remains incurable. An emerging trend to improve clinical outcomes in MM involves harnessing the inherent ability of NK cells to kill malignant cells by recruiting NK cells and enhancing their cytotoxicity toward the malignant MM cells. Following the clinical success of blocking T cell IRs in multiple cancers, targeting NK cell IRs is drawing increasing attention. Relevant NK cell IRs that are attractive candidates for checkpoint blockades include KIRs, NKG2A, LAG-3, TIGIT, PD-1, and TIM-3 receptors. Investigating these NK cell IRs as pathogenic agents and therapeutic targets could lead to promising applications in MM therapy. This review describes the critical role of enhancing NK cell activity in MM and discusses the potential of blocking NK cell IRs as a future MM therapy.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".