KLRC1 knockout overcomes HLA-E-mediated inhibition and improves NK cell antitumor activity against solid tumors
Bibliographic record
Abstract
Introduction Natural Killer (NK) cells hold the potential to shift cell therapy from a complex autologous option to a universal off-the-shelf one. Although NK cells have demonstrated efficacy and safety in the treatment of leukemia, the limited efficacy of NK cell-based immunotherapies against solid tumors still represents a major hurdle. In the immunosuppressive tumor microenvironment (TME), inhibitory interactions between cancer and immune cells impair antitumoral immunity. KLRC1 gene encodes the NK cell inhibitory receptor NKG2A, which is a potent NK cell immune checkpoint. NKG2A specifically binds HLA-E, a non-classical HLA class I molecule frequently overexpressed in tumors, leading to the transmission of inhibitory signals that strongly impair NK cell function. Methods To restore NK cell cytotoxicity against HLA-E + tumors, we have targeted the NKG2A/HLA-E immune checkpoint by using a CRISPR-mediated KLRC1 gene editing. Results KLRC1 knockout resulted in a reduction of 81% of NKG2A + cell frequency in ex vivo expanded human NK cells post-cell sorting. In vitro , the overexpression of HLA-E by tumor cells significantly inhibited wild-type (WT) NK cell cytotoxicity with p -values ranging from 0.0071 to 0.0473 depending on tumor cell lines. In contrast, KLRC1 KO NK cells exhibited significantly higher cytotoxicity when compared to WT NK cells against four different HLA-E + solid tumor cell lines, with p -values ranging from<0.0001 to 0.0154. Interestingly, a proportion of 43.5% to 60.2% of NKG2A − NK cells within the edited NK cell population was sufficient to reverse at its maximum the HLA-E-mediated inhibition of NK cell cytotoxicity. The expression of the activating receptor NKG2C was increased in KLRC1 KO NK cells and contributed to the improved NK cell cytotoxicity against HLA-E + tumors. In vivo , the adoptive transfer of human KLRC1 KO NK cells significantly delayed tumor progression and increased survival in a xenogeneic mouse model of HLA-E + metastatic breast cancer, as compared to WT NK cells ( p = 0.0015). Conclusions Our results demonstrate that KLRC1 knockout is an effective strategy to improve NK cell antitumor activity against HLA-E + tumors and could be applied in the development of NK cell therapy for solid tumors.
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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 | 0.001 |
| 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".