Influenza-Specific T-Cell Responses to Vaccination Are Independent of Underlying Hematological Malignancy: Analysis of a Randomized Influenza Vaccination Trial
Bibliographic record
Abstract
BACKGROUND: There are few in-depth immunogenicity analyses of novel influenza vaccination strategies in high-risk patients with hematological malignancy (HM). METHODS: Participants receiving treatment for active HM (multiple myeloma [MM], chronic lymphocytic leukemia [CLL], or non-Hodgkin lymphoma [NHL]) in a randomized controlled trial of 2 doses of adjuvanted quadrivalent inactivated influenza vaccine (QIV) versus 2 doses of standard-dose QIV during 2022 were included. Hemagglutination (HA) inhibition assay and HA probe-specific B-cells were compared at baseline and 1, 2, and 6 months after the first vaccine dose (visits 1-4). A subset underwent ex vivo live virus infection of peripheral blood mononuclear cells at visits 1 and 3 with A/H1N1 and A/H3N2 to assess interferon (IFN) γ-producing CD4+ T cells, CD8+ T cells, natural killer cells, CD161+TRAV1-2+ mucosal-associated invariant T (MAIT)-like T cells and γδ T cells. RESULTS: In total, 62 patients with HM were analyzed (32 in the adjuvanted-dose and 30 in the standard-dose group), 13 (21.0%) with CLL, 24 (38.7%) MM, and 25 (40.3%) with NHL. Participants with MM had higher geometric mean antibody titers (P < .001) and influenza-specific B-cell responses for H1, H3, and B/Victoria at visits 2 and 3 than those with CLL or NHL (P < .05). The total CD19+ B-cell and HA probe-specific B-cell counts were found to significantly predict seroconversion at visits 2 and 3. Overall, with vaccination, there was an increase in the percentage frequency of B/Victoria influenza-specific B-cells (P = .01), IFN-γ-producing CD4+ T cells (P = .01) for A/H1N1 and IFN-γ-producing MAIT-like cells (P = .003) for A/H3N2. CONCLUSIONS: Influenza strain-specific cellular responses were detectable following vaccination despite expected B-cell depletion in patients receiving active treatment for HM. CLINICAL TRIALS REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12622000454774.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".