Whole blood‐based measurement of SARS‐CoV‐2‐specific T cells reveals asymptomatic infection and vaccine immunogenicity in healthy subjects and patients with solid‐organ cancers
Bibliographic record
Abstract
Abstract Accurate assessment of SARS‐CoV‐2 immunity is critical in evaluating vaccine efficacy and devising public health policies. Whilst the exact nature of effective immunity remains incompletely defined, SARS‐CoV‐2‐specific T‐cell responses are a critical feature that will likely form a key correlate of protection against COVID‐19. Here, we developed and optimized a high‐throughput whole blood‐based assay to determine the T‐cell response associated with prior SARS‐CoV‐2 infection and/or vaccination amongst 231 healthy donors and 68 cancer patients. Following overnight in vitro stimulation with SARS‐CoV‐2‐specific peptides, blood plasma samples were analysed for T H 1‐type cytokines. Highly significant differential IFN‐γ + /IL‐2 + SARS‐CoV‐2‐specific T‐cell responses were seen amongst previously infected COVID‐19‐positive healthy donors in comparison with unknown / naïve individuals ( p < 0·0001). IFN‐γ production was more effective at identifying asymptomatic donors, demonstrating higher sensitivity (96·0% vs. 83·3%) but lower specificity (84·4% vs. 92·5%) than measurement of IL‐2. A single COVID‐19 vaccine dose induced IFN‐γ and/or IL‐2 SARS‐CoV‐2‐specific T‐cell responses in 116 of 128 (90·6%) healthy donors, reducing significantly to 27 of 56 (48·2%) when measured in cancer patients ( p < 0·0001). A second dose was sufficient to boost T‐cell responses in the majority (90·6%) of cancer patients, albeit IFN‐γ + responses were still significantly lower overall than those induced in healthy donors ( p = 0·034). Three‐month post‐vaccination T‐cell responses also declined at a faster rate in cancer patients. Overall, this cost‐effective standardizable test ensures accurate and comparable assessments of SARS‐CoV‐2‐specific T‐cell responses amenable to widespread population immunity testing, and identifies individuals at greater need of booster vaccinations.
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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 | 0.001 |
| 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) | 0.002 | 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".