How do we manufacture clinical‐grade interleukin‐15–stimulated natural killer cell products for cancer treatment?
Bibliographic record
Abstract
BACKGROUND: Cancer immunotherapy involving natural killer (NK) cells has gained interest. Here we report two methods to obtain interleukin (IL)-15-activated NK cells for clinical use. STUDY DESIGN AND METHODS: IL-15-activated NK cell products were obtained after 1) enrichment from healthy haploidentical donors' peripheral blood mononuclear cells (PBMNCs) collected by nonmobilized apheresis by a two-step magnetic procedure, depletion of CD3+ cells followed by selection of CD56+ cells and ex vivo overnight stimulation with IL-15 (NKIL15); and 2) expansion using the K562-mb15-41BBL cell line (NKAE), from autologous PBMNCs from patients with multiple myeloma or expansion from healthy haploidentical PBMNCs obtained from whole blood using the same previous cell line. We analyzed the NK cell recovery and expansion, T cell depletion, phenotype, cytotoxicity, safety, and genomic stability of two good manufacturing practices (GMP)-grade IL-15-activated NK cell products. RESULTS: The number of NK cells obtained from NKIL15 cell and NKAE cell products was similar; however, there were significantly fewer T cells in the NKIL15 cell product. The haploidentical NKAE cell product contained more T cells than the autologous NKAE cell product. The surface expression of the activating receptors CD69, CD25, natural killer group-2 member D receptor, NKp44, NKp46, NKp30, and DNA accessory molecule 1 was up regulated in both NK cell products. NKIL15 cell and NKAE cell products had significantly higher lytic activity than unstimulated NK cells and showed no lytic activity against PBMNCs from healthy donors. No genetic alterations or potential oncogenic effects were found. CONCLUSION: Different GMP-grade procedures can be used to obtain large numbers of highly IL-15-activated NK cells with extremely low T cell content for clinical use.
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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 | 0.003 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".