Human Granzyme K Is a Feature of Innate T Cells in Blood, Tissues, and Tumors, Responding to Cytokines Rather than TCR Stimulation
Bibliographic record
Abstract
NK cells and CD8 T cells use cytotoxic molecules to kill virally infected and tumor cell targets. While perforin and granzyme B (GzmB) are the most commonly studied lytic molecules, less is known about granzyme K (GzmK). However, this granzyme has been recently associated with improved prognosis in solid tumors. In this study, we show that, in humans, GzmK is predominantly expressed by innate-like lymphocytes, as well as a newly identified population of GzmK+CD8+ non- mucosal-associated invariant T cells with innate-like characteristics. We found that GzmK+ T cells are KLRG1+EOMES+IL-7R+CD62L-Tcf7int, suggesting that they are central memory T and effector memory T cells. Furthermore, GzmK+ cells are absent/low in cord blood, suggesting that GzmK is upregulated with immune experience. Surprisingly, GzmK+ cells respond to cytokine stimuli alone, whereas TCR stimulation downregulates GzmK expression, coinciding with GzmB upregulation. GzmK+ cells have reduced IFN-γ production compared with GzmB+ cells in each T cell lineage. Collectively, this suggests that GzmK+ cells are not naive, and they may be an intermediate memory-like or preterminally differentiated population. GzmK+ cells are enriched in nonlymphoid tissues such as the liver and adipose. In colorectal cancer, GzmK+ cells are enriched in the tumor and can produce IFN-γ, but GzmK+ expression is mutually exclusive with IL-17a production. Thus, in humans, GzmK+ cells are innate memory-like cells that respond to cytokine stimulation alone and may be important effector cells in the tumor.
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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) | 0.003 | 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".