Phase I Study of Random Healthy Donor–Derived Allogeneic Natural Killer Cell Therapy in Patients with Malignant Lymphoma or Advanced Solid Tumors
Bibliographic record
Abstract
Natural killer (NK) cells with mismatched killer cell immunoglobulin-like receptor-ligand pairs have shown efficacy and been proven safe in treatment of cancer patients. Ex vivo-expanded and highly activated NK cells (MG4101) had been generated under good manufacturing practice conditions, which demonstrated potent anticancer activity in vitro and in vivo in preclinical studies. The current phase I clinical trial was designed to evaluate safety and possible clinical efficacy of repetitive administrations of MG4101 derived from random unrelated healthy donors into patients with malignant lymphoma or advanced, recurrent solid tumors. The maximum dose (3 × 10(7) cells/kg, triple infusion) was tolerable without significant adverse events. Of 17 evaluable patients, 8 patients (47.1%) showed stable disease and 9 (52.9%) showed progressive disease. We also evaluated the capacity of MG4101 to influence host immune responses. Administration of MG4101 augmented NKG2D expression on CD8(+) T cells and upregulated chemokines that recruit T cells. In contrast, administration of MG4101 reduced regulatory T cells and myeloid-derived suppressor cells and suppressed TGFβ production. In conclusion, administration of a large number of MG4101 cells was not only safe and feasible, but also exhibited efficacy in maintaining the effector arm of the host immune response.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".