CD40L and IL-4 suppress NK cell-mediated antibody-dependent cellular cytotoxicity through the HLA-E:NKG2A axis
Bibliographic record
Abstract
Abstract Background Anti-CD20 antibodies are first-line treatments for B cell malignancies. Natural killer (NK) cells are important mediators of anti-CD20 antibody efficacy in humans through antibody-dependent cellular cytotoxicity (ADCC). In B cell malignancies, the lymph nodes are a critical site of pathology and the T cell-derived signals CD40L and IL-4 within the lymph node microenvironment can mediate tumour proliferation, survival and resistance to pro-apoptotic therapy. CD40L and IL-4 have recently been shown to inhibit NK cell activation against chronic lymphocytic leukaemia (CLL) cells via the HLA-E:NKG2A immune checkpoint axis. However, the effect of these signals on NK cell-mediated ADCC of malignant B cells is unclear. Methods Using a combination of clinical samples, murine models, flow cytometry, immunoblotting, immunohistochemistry, ELISA, bioinformatics and functional assays, we examined the impact of lymph node-mimicking conditions on NK cell-mediated ADCC against malignant B cells. Exogenous CD40L and IL-4 were used to mimic T-B cell interactions in 2D malignant B cell cultures, in addition to a 3D spheroid model of T cell-dependent CLL proliferation. Results CD40L and IL-4 increased HLA-E expression on the surface of primary CLL cells and non-Hodgkin’s lymphoma (NHL) cell lines, and this decreased NK cell-mediated ADCC via ligation of the inhibitory receptor NKG2A. High HLA-E surface expression was observed in lymph node FFPE sections of CLL and NHL patients and in a 3D ex vivo lymph node-mimicking model of CLL. NKG2A blockade potentiated NK cell-mediated ADCC against malignant B cells treated with CD40L and IL-4 and improved anti-CD20 antibody therapy in a murine model of B cell lymphoma. Conclusion These results reveal a novel mechanism of resistance to anti-CD20 therapy in B cell malignancies and demonstrate that the combination of anti-NKG2A with anti-CD20 could improve the treatment of patients with CLL or NHL.
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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) | 0.002 | 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".