Related Experiment Video
Updated: Aug 9, 2026
Rapid In Vivo Assessment of Adjuvant's Cytotoxic T Lymphocytes Generation Capabilities for Vaccine Development
Published on: June 19, 2018
Analyzing cellular immunogenicity in vaccine clinical trials: a new statistical method including non-specific
Edouard Lhomme1, Boris P Hejblum2, Christine Lacabaratz3
1Univ. Bordeaux, Department of Public Health, Inserm Bordeaux Population Health Research Centre, Inria SISTM, F-33000 Bordeaux, France; Vaccine Research Institute (VRI), Créteil F-94000, France; Pôle de Santé Publique, CHU de Bordeaux, Bordeaux F-33000, France; Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, CHU Bordeaux, CIC 1401, EUCLID/F-CRIN Clinical Trials Platform, F-33000 Bordeaux, France.
Insights
A new statistical method using bivariate linear regression improves T-cell response analysis in vaccine trials. This approach accurately estimates antigen-specific T-cell immunity without bias from non-specific responses, enhancing vaccine immunogenicity evaluation.
Area of Science:
- Immunology
- Biostatistics
- Vaccinology
Background:
- Vaccine immunogenicity is crucial for development, with T-cell responses often measured using intracellular cytokine staining (ICS).
- Conventional analysis of ICS data involves subtracting non-specific responses, which can introduce statistical issues like measurement error and reduced power.
- Accurate assessment of T-cell responses is vital for understanding vaccine efficacy and guiding future vaccine design.
Purpose of the Study:
- To introduce and evaluate a novel statistical approach for analyzing ICS data in vaccine trials.
- To address the methodological limitations of conventional subtraction methods for non-specific responses.
- To provide a more robust and flexible method for estimating antigen-specific T-cell responses.
Main Methods:
- Development of a bivariate linear regression model to simultaneously estimate non-specific and antigen-specific ICS responses.
- Benchmarking the proposed model against conventional methods using simulated data for bias and error control.
- Application of the model to real-world pre- and post-vaccination data from two HIV vaccine trials (ANRS VRI01 and VRI02).
Main Results:
- The bivariate model demonstrated comparable statistical performance to conventional methods across various simulation scenarios.
- Unlike conventional approaches, the proposed model provided robust results consistently across all scenarios.
- The bivariate model accurately estimated antigen-specific T-cell responses, independent of the non-specific response, regardless of their correlation.
Conclusions:
- The novel bivariate modeling approach offers a more flexible and accurate method for analyzing T-cell immunogenicity data from vaccine trials.
- This method overcomes limitations of conventional subtraction techniques, leading to more detailed results and precise interpretation of vaccine-induced T-cell responses.
- The findings support the adoption of this bivariate approach for improved evaluation of vaccine immunogenicity in clinical development.
Abstract:
Evaluation of immunogenicity is a key step in the clinical development of novel vaccines. T-cell responses to vaccine candidates are typically assessed by intracellular cytokine staining (ICS) using multiparametric flow cytometry. A conventional statistical approach to analyze ICS data is to compare, between vaccine regimens or between baseline and post-vaccination of the same regimen depending on the trial design, the percentages of cells producing a cytokine of interest after ex vivo stimulation of peripheral blood mononuclear cells (PBMC) with vaccine antigens, after subtracting the non-specific response (of unstimulated cells) of each sample. Subtraction of the non-specific response is aimed at capturing the specific response to the antigen, but raises methodological issues related to measurement error and statistical power. We describe here a new statistical approach to analyze ICS data from vaccine trials. We propose a bivariate linear regression model for estimating the non-specific and antigen-specific ICS responses. We benchmarked the performance of the model in terms of both bias and control of type-I and -II errors in comparison with conventional approaches, and applied it to simulated data as well as real pre- and post-vaccination data from two recent HIV vaccine trials (ANRS VRI01 in healthy volunteers and therapeutic VRI02 ANRS 149 LIGHT in HIV-infected participants). The model was as good as the conventional approaches (with or without subtraction of the non-specific response) in all simulation scenarios in terms of statistical performance, whereas the conventional approaches did not provide robust results across all scenarios. The proposed model estimated the T-cell responses to the antigens without any effect of the non-specific response on the specific response, irrespective of the correlation between the non-specific and specific responses. This novel method of analyzing T-cell immunogenicity data based on bivariate modeling is more flexible than conventional methods, and so yields more detailed results and enables accurate interpretation of vaccine-induced response.
