Effects of Antibody Response after Booster Vaccination on SARS-CoV-2 Breakthrough Infections and Disease Outcomes in Advanced Cancer Patients: A Prospective Analysis of the Vax-on-Third Study
Bibliographic record
Abstract
(1) Background: The clinical implications of COVID-19 outbreaks following SARS-CoV-2 vaccination in immunocompromised recipients are a worldwide concern. Cancer patients on active treatment remain at an increased risk of developing breakthrough infections because of waning immunity and the emergence of SARS-CoV-2 variants. There is a paucity of data on the effects of COVID-19 outbreaks on long-term survival outcomes in this population. (2) Methods: We enrolled 230 cancer patients who were on active treatment for advanced disease and had received booster dosing of an mRNA-BNT162b2 vaccine as part of the Vax-On-Third trial between September 2021 and October 2021. Four weeks after the third immunization, IgG antibodies against the spike receptor domain of SARS-CoV-2 were tested in all patients. We prospectively evaluated the incidence of breakthrough infections and disease outcomes. The coprimary endpoints were the effects of antibody titers on the development of breakthrough infections and the impact of COVID-19 outbreaks on cancer treatment failure. (3) Results: At a median follow-up of 16.3 months (95% CI 14.5–17.0), 85 (37%) patients developed SARS-CoV-2 infection. Hospitalization was required in 11 patients (12.9%) and only 2 (2.3%) deaths related to COVID-19 outbreaks were observed. Median antibody titers were significantly lower in breakthrough cases than in non-cases (291 BAU/mL (95% CI 210–505) vs. 2798 BAU/mL (95% CI 2323–3613), p < 0.001). A serological titer cut-off below 803 BAU/mL was predictive of breakthrough infection. In multivariate testing, antibody titers and cytotoxic chemotherapy were independently associated with an increased risk of outbreaks. Time-to-treatment failure after booster dosing was significantly shorter in patients who contracted SARS-CoV-2 infection (3.1 months (95% CI 2.3–3.6) vs. 16.2 months (95% CI 14.3–17.0), p < 0.001) and had an antibody level below the cut-off (3.6 months (95% CI 3.0–4.5) vs. 14.6 months (95% CI 11.9–16.3), p < 0.001). A multivariate Cox regression model confirmed that both covariates independently had a worsening effect on time-to-treatment failure. (4) Conclusions: These data support the role of vaccine boosters in preventing the incidence and severity of COVID-19 outbreaks. Enhanced humoral immunity after the third vaccination significantly correlates with protection against breakthrough infections. Strategies aimed at restraining SARS-CoV-2 transmission in advanced cancer patients undergoing active treatment should be prioritized to mitigate the impact on disease outcomes.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".