IL-2 produced by HBV-specific T cells as a biomarker of viral control and predictor of response to PD-1 therapy across clinical phases of chronic hepatitis B
Bibliographic record
Abstract
BACKGROUND: There are no immunological biomarkers that predict control of chronic hepatitis B (CHB). The lack of immune biomarkers raises concerns for therapies targeting PD-1/PD-L1 because they have the potential for immune-related adverse events. Defining specific immune functions associated with control of HBV replication could identify patients likely to respond to anti-PD-1/PD-L1 therapies and achieve a durable functional cure. METHODS: We enrolled immunotolerant, HBeAg+ immune-active (IA+), HBeAg- immune-active (IA-), inactive carriers, and functionally cured patients to test ex vivo PD-1 blockade on HBV-specific T cell functionality. Peripheral blood mononuclear cells were stimulated with overlapping peptides covering HBV proteins +/-α-PD-1 blockade. Functional T cells were measured using a 2-color FluoroSpot assay for interferon-γ and IL-2. Ex vivo functional restoration was compared to the interferon response capacity assay, which predicts overall survival in cancer patients receiving checkpoint inhibitors. RESULTS: Ex vivo interferon-γ+ responses did not differ across clinical phases. IL-2+ responses were significantly higher in patients with better viral control and preferentially restored with PD-1 blockade. Inactive carrier patients displayed the greatest increase in IL-2 production, which was dominated by CD4 T cell and response to the HBcAg. The interferon response capacity assay significantly correlated with the degree of HBV-specific T cell restoration. CONCLUSIONS: IL-2 production was associated with better HBV control and superior to interferon-γ as a marker of T cell restoration following ex vivo PD-1 blockade. Our study suggests that responsiveness to ex vivo PD-1 blockade, or the interferon response capacity assay, may support stratification for α-PD-1 therapies.
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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 | 0.001 | 0.001 |
| 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".