CD16<sup>+</sup>NK-92 and anti-CD123 monoclonal antibody prolongs survival in primary human acute myeloid leukemia xenografted mice
Bibliographic record
Abstract
Patients with acute myeloid leukemia (AML) often relapse after initial therapy because of persistence of leukemic stem cells that frequently express the IL-3 receptor alpha chain CD123. Natural killer (NK) cell-based therapeutic strategies for AML show promise and we explore the NK cell lines, NK-92 and CD16+ NK-92, as a treatment for AML. NK-92 has been tested in phase I clinical trials with minimal toxicity; irradiation prior to infusion prevents risk of engraftment. The CD16 negative NK-92 parental line was genetically modified to express the high affinity Fc gamma receptor, enabling antibody-dependent cell-mediated cytotoxicity, which we utilized in combination with an anti-CD123 antibody to target leukemic stem cells. NK-92 was preferentially cytotoxic against leukemic stem and progenitor cells compared with bulk leukemia in in vitro assays, while CD16+ NK-92 in combination with an anti-CD123 mAb mediated antibody-dependent cell-mediated cytotoxicity against CD123+ leukemic targets. Furthermore, NK-92 infusions (with or without prior irradiation) improved survival in a primary AML xenograft model. Mice xenografted with primary human AML cells had a superior survival when treated with irradiated CD16+NK-92 cells and an anti-CD123 monoclonal antibody (7G3) versus treatment with irradiated CD16+NK-92 cells combined with an isotype control antibody. In this proof-of-principle study, we show for the first time that a CD16+NK-92 cell line combined with an antibody that targets a leukemic stem cell antigen can lead to improved survival in a relevant pre-clinical model of AML.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".