CRISPR Screens Identify Key Regulators of NK Cell Cytotoxicity in Cancer Therapy
Bibliographic record
Abstract
Abstract Natural Killer (NK) cells are innate lymphocytes that exhibit cytotoxic activity against cancer cells and are associated with a good prognosis when present in solid tumors. In the past, NK cells have been used in adoptive cell transfer to treat various cancers with modest relative success. More recently, NK cells modified with chimeric antigen receptor (CAR-NK cells) are being explored as a potential off-the-shelf therapy. However, mechanisms preventing the full cytotoxic anti-tumor activity of NK cells remain understudied. To unveil the key genes that govern the activation and inhibition of the cytotoxic activity of NK cells towards cancer cells, we performed whole-genome CRISPR screens in NK cells. Primary activated NK cells from the NK-cell Activation and Expansion System (NKAES) were transduced with the Yusa CRISPR library and then electroporated with Cas9 protein, inducing the knockout (KO) of 18,010 genes. These NK cells were exposed to the colorectal cancer cell line HT-29 for 4 hours. After co-culture, NK cells were sorted based on the expression of the degranulation marker CD107a and the production of IFNg, both associated with NK cell activation. To identify genes involved in the inhibition of NK cell activity, we will compare sgRNA enriched in CD107a+ and IFNg+ cells to unactivated negative cells. In turn, these genes can be therapeutically targeted or genetically modified in future iPSC-derived NK and CAR-NK cells to improve the outcome of cancer patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".