Natural Killer Cell as Effectors in Chimeric Antigen Receptor Based Immunotherapies for Cancer
Bibliographic record
Abstract
Recent developments in the expansion and manipulation of primary NK cells has allowed this source of effective anti-tumour cells to be exploited for cell-based cancer immunotherapies. While ex vivo expanded primary NK cells are highly effective in the treatment of haematological malignancies, their efficacy against the solid tumour has been limited due to the presence of immune-regulatory factors in the tumour microenvironment. These factors can abrogate NK cell function by down regulating the expression of NK activating receptors, thus preventing these highly cytotoxic effector cells from activating in response to tumour challenge. Our work explores whether the expression of a tumour specific chimeric antigen receptor (CAR) on ex vivo expanded primary NK cells would allow the lost activatory signalling to be recouped, and regain their efficacy against the solid tumour. Unfortunately, the use of primary NK cells as effectors in CAR based cell immunotherapies has been hampered by the technical limitations of producing large numbers of CAR positive primary NK cells. This has led many researchers to utilise the NK-92 cell line instead of primary cells. We demonstrate that ex vivo expanded primary CAR NK cells can be produced efficiently and demonstrate higher anti-tumour functionality than CAR NK-92. Finally, due to the intricacies of NK cell biology, they are able to effectively discriminate between healthy and malignant targets thus preventing their cytotoxic function from being directed towards the incorrect target. This could be a key advantage in the use of primary NK cells over T cells as effectors of CAR as the off-tumour/on-target adverse effects seen with CAR T cells has severely hampered this clinical strategy. We have shown that CAR T cells but not CAR NK cells are reactive towards phenotypically non-malignant, clinically relevant, healthy cells expressing the CAR target.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".