Immunoregulatory NK cells in non small cell lung cancer (NSCLC) 3221
Bibliographic record
Abstract
Abstract Description Despite significant advancements in targeted therapies and immunotherapies, a substantial proportion of non-small cell lung cancer (NSCLC) patients remain unresponsive to treatment. One contributing factor is tumor-induced immunosuppression, which impairs effective T cell responses. Natural killer (NK) cells, while playing a pivotal role in antitumor immunity, have been shown to negatively regulate adaptive immune responses in various contexts. In this study, we characterized the phenotype of NK cells within the NSCLC microenvironment and investigated their potential contribution to immune suppression. CD3- CD56+ NK cells were isolated from tumor resection specimens and profiled using single-cell RNA sequencing and CITE-seq. Identified NK cell clusters were subsequently quantified in tumor-infiltrating lymphocyte (TIL) cultures exhibiting different expansion rates. Our analyses identified a tumor-specific NK cell subset associated with reduced recurrence-free survival (RFS). This subset was preferentially enriched in TIL cultures with low expansion rates compared to those with high expansion rates, suggesting a potential role in limiting T cell proliferation. Future investigations will focus on confirming the regulatory role of this NK cell subset and elucidating the mechanisms underlying its suppressive function. Topic Categories Tumor Immunology: Cellular Responses and Tumor Microevironment (TIME)
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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 | 0.001 |
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".