Tyrosine kinase pathways modulate tumor susceptibility to natural killer cells
Bibliographic record
Abstract
Natural killer (NK) cells are primary effectors of innate immunity directed against transformed tumor cells. In response, tumor cells have developed mechanisms to evade NK cell-mediated lysis through molecular mechanisms that are not well understood. In the present study, we used a lentiviral shRNA library targeting more than 1,000 human genes to identify 83 genes that promote target cell resistance to human NK cell-mediated killing. Many of the genes identified in this genetic screen belong to common signaling pathways; however, none of them have previously been known to modulate susceptibility of human tumor cells to immunologic destruction. Gene silencing of two members of the JAK family (JAK1 and JAK2) increased the susceptibility of a variety of tumor cell types to NK-mediated lysis and induced increased secretion of IFN-γ by NK cells. Treatment of tumor cells with JAK inhibitors also increased susceptibility to NK cell activity. These findings may have important clinical implications and suggest that small molecule inhibitors of tyrosine kinases being developed as therapeutic antitumor agents may also have significant immunologic effects in vivo.
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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".