The effect of blocking immune checkpoints LAG-3 and PD-1 on human invariant Natural Killer T cell function
Bibliographic record
Abstract
Invariant Natural Killer T (iNKT) cells undergo immune exhaustion during chronic activation caused by cancer and viral infections, such as HIV. Exhaustion is marked by cell dysfunction and increased expression of immune checkpoint proteins programmed cell-death-1 (PD-1) and lymphocyte-activation-gene-3 (LAG-3). We hypothesize that blockade of PD-1 and/or LAG-3 will enhance iNKT cell function. Utilizing peripheral blood mononuclear cells from healthy donors, LAG-3 and PD-1 expression on iNKT cells was assessed using flow cytometry following in vitro stimulation with iNKT-specific stimulant α-galactosylceramide (n = 4). Efficacy of anti-LAG-3 and/or anti-PD-1 antibody blockades in enhancing iNKT cell function was assessed by determining proliferative capacity and IFN-γ production (n = 9). LAG-3 and PD-1 expression on iNKT cells peaked at Day 4 (98.8%; p ≤ 0.0001 and 98.8%; p = 0.005, respectively), followed by steep decrease by Day 10, coinciding with peak iNKT cell proliferation. In a 10-day blocking assay, both the anti-PD-1 alone and dual anti-PD-1 and anti-LAG-3 significantly increased iNKT proliferation (6 and 6.29 log2 fold-change respectively) compared to the no blockade control (ANOVA-p = 0.0005) with the dual blockade system being more effective (t-test-p = 0.013). This provides proof-of-concept for LAG-3 and PD-1 as immunotherapeutic targets to enhance human iNKT cell function, with the long-term goal of addressing immune exhaustion.
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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 | 0.001 |
| 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".