Induction of an Immune-Protective T-Cell Repertoire With Diverse Genetic Coverage by a Novel Viral-Vectored Tuberculosis Vaccine in Humans
Bibliographic record
Abstract
Whether a candidate tuberculosis vaccine induces clinically relevant protective T-cell repertoires in humans will not be known until the completion of costly efficacy clinical trials. We have developed an integrated immunologic approach to investigate the clinical relevance of T cells induced by a novel tuberculosis vaccine in a phase 1 trial. This approach consists of screening for likely dominant T-cell epitopes, establishing antigen-specific memory T-cell lines for identifying CD8+ and CD4+ T-cell epitopes, determining the ability of vaccine-induced T cells to inhibit mycobacterial growth in infected cells, and examining the genetic diversity of HLA recognition and the clinical relevance of identified T-cell epitopes. A single-dose immunization in BCG-primed adults with an adenovirus-based tuberculosis vaccine elicits a repertoire of memory T cells capable of recognizing multiple Ag85A epitopes. These T cells are polyfunctional and cytotoxic and can inhibit mycobacterial growth in infected target cells. Some identified T-cell epitopes are promiscuous and recognizable by the common HLA alleles. These epitopes are clinically relevant to the epitopes identified in people with latent Mycobacterium tuberculosis infection and treated patients with tuberculosis. These data support further clinical development of this candidate vaccine. Our approach helps fill the gap in clinical tuberculosis vaccine development.
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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 | 0.000 |
| 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".