Humanized anti-CD123 antibody facilitates NK cell antibody-dependent cell-mediated cytotoxicity (ADCC) of Hodgkin lymphoma targets via ARF6/PLD-1
Bibliographic record
Abstract
Abstract CD123 (IL-3Rα) is frequently expressed by malignant Hodgkin lymphoma (HL) cells. Naked monoclonal antibodies (mAb) against HL lack clinical benefit, partially due to absence of natural killer (NK) cells in the tumor microenvironment. Here we show that the combination of a fully humanized anti-CD123 mAb (CSL362) and high-affinity Fcγ-receptor NK-92 cells (haNK) effectively target and kill HL cells in vitro. First, we confirmed high expression of CD123 in 2 of the 3 HL cell lines (KM-H2 and L-428), and its absence in NK cells. Cytotoxicity of haNK cells against CD123-positive HL cells was significantly higher in the presence of CSL362. This was also shown with IL-15-activated primary NK cells, although haNK cells showed a 10.87-fold lower estimated half-maximal stimulatory effective concentration (EC50). CSL362 facilitated a significant increase in the expression of CD107a, intracellular IFN-γ and TNF-α and enhanced expression ofc-JUN,PLD-1, andARF6by NK cells. Inhibition of the ARF6–PLD-1 axis (NAV2729), but not of the MAPK pathway (U0126), completely abrogated CSL362-facilitated antibody-dependent cell-mediated cytotoxicity (ADCC) in haNK and activated primary NK cells. Our results support CD123 as an immunotherapeutic target for HL and the combination of NK cells and CSL362 as a treatment strategy for HL.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".