Determining vaccine responders in the presence of baseline immunity using single-cell assays and paired control samples

A key objective in vaccine studies is to evaluate vaccine-induced immunogenicity and determine whether participants have mounted a response to the vaccine. Cellular immune responses are essential for assessing vaccine-induced immunogenicity, and single-cell assays, such as intracellular cytokine staining (ICS) and B-cell phenotyping (BCP), are commonly employed to profile individual immune cell phenotypes and the cytokines they produce after stimulation. In this article, we introduce a novel statistical framework for identifying vaccine responders using ICS data collected before and after vaccination. This framework incorporates paired control data to account for potential unintended variations between assay runs, such as batch effects, that could lead to misclassification of participants as vaccine responders or non-responders. To formally integrate paired control data for accounting for assay variation across different time points (ie before and after vaccination), our proposed framework calculates and reports two $ P $-values, both adjusting for paired control data but in distinct ways: (i) the maximally adjusted $ P $-value, which applies the most conservative adjustment to the unadjusted $ P $-value, ensuring validity over all plausible batch effects consistent with the paired control samples' data, and (ii) the minimally adjusted $ P $-value, which imposes only the minimal adjustment to the unadjusted $ P $-value, such that the adjusted $ P $-value cannot be falsified by the paired control samples' data. Minimally and maximally adjusted $ P $-values offer a balanced approach to managing Type I error rates and statistical power in the presence of batch effects. We apply this framework to analyze ICS data collected at baseline and 4 wks post-vaccination from the COVID-19 Prevention Network (CoVPN) 3008 study. Our analysis helps address two clinical questions: (i) which participants exhibited evidence of an incident Omicron infection between baseline and 4 wks after receiving the final dose of the primary vaccination series, and (ii) which participants showed vaccine-induced T cell responses against the Omicron BA.4/5 Spike protein.

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Publication Details

Journal
Biostatistics
Published
2024-12-31
DOI
https://doi.org/10.1093/biostatistics/kxaf045
Primary Topic
SARS-CoV-2 and COVID-19 Research
Type
article
Field-Weighted Citation Impact
0.00
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article

Determining vaccine responders in the presence of baseline immunity using single-cell assays and paired control samples

Asa Tapley, Siyu Heng, Stephen De Rosa, Bo Zhang et al.
Biostatistics
SARS-CoV-2 and COVID-19 Research
article

Determining vaccine responders in the presence of baseline immunity using single-cell assays and paired control samples

Asa Tapley, Siyu Heng, Stephen De Rosa, Bo Zhang, Zhe Chen
article en

Abstract

A key objective in vaccine studies is to evaluate vaccine-induced immunogenicity and determine whether participants have mounted a response to the vaccine. Cellular immune responses are essential for assessing vaccine-induced immunogenicity, and single-cell assays, such as intracellular cytokine staining (ICS) and B-cell phenotyping (BCP), are commonly employed to profile individual immune cell phenotypes and the cytokines they produce after stimulation. In this article, we introduce a novel statistical framework for identifying vaccine responders using ICS data collected before and after vaccination. This framework incorporates paired control data to account for potential unintended variations between assay runs, such as batch effects, that could lead to misclassification of participants as vaccine responders or non-responders. To formally integrate paired control data for accounting for assay variation across different time points (ie before and after vaccination), our proposed framework calculates and reports two $ P $-values, both adjusting for paired control data but in distinct ways: (i) the maximally adjusted $ P $-value, which applies the most conservative adjustment to the unadjusted $ P $-value, ensuring validity over all plausible batch effects consistent with the paired control samples' data, and (ii) the minimally adjusted $ P $-value, which imposes only the minimal adjustment to the unadjusted $ P $-value, such that the adjusted $ P $-value cannot be falsified by the paired control samples' data. Minimally and maximally adjusted $ P $-values offer a balanced approach to managing Type I error rates and statistical power in the presence of batch effects. We apply this framework to analyze ICS data collected at baseline and 4 wks post-vaccination from the COVID-19 Prevention Network (CoVPN) 3008 study. Our analysis helps address two clinical questions: (i) which participants exhibited evidence of an incident Omicron infection between baseline and 4 wks after receiving the final dose of the primary vaccination series, and (ii) which participants showed vaccine-induced T cell responses against the Omicron BA.4/5 Spike protein.

BiostatisticsVol. 26(1)
Seattle Pacific University (US), Cape Town HVTN Immunology Laboratory / Hutchinson Centre Research Institute of South Africa (ZA), University of Cape Town (ZA), University of Washington (US), Fred Hutch Cancer Center (US), New York University (US), University of Pennsylvania (US)
Openalex Percentile: Top 66%
SARS-CoV-2 and COVID-19 Research
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