tetano
Editor, Senior Moderator
PLoS Comput Biol
. 2025 Nov 14;21(11):e1013720.
doi: 10.1371/journal.pcbi.1013720. Online ahead of print. Different antigenic distance metrics generate similar predictions of influenza vaccine response breadth despite moderate correlation
W Zane Billings[SUP] 1 2 [/SUP], Yang Ge[SUP] 1 [/SUP], Amanda L Skarlupka[SUP] 3 [/SUP], Savannah L Miller[SUP] 1 2 [/SUP], Hayley Hemme[SUP] 1 2 [/SUP], Murphy John[SUP] 1 2 [/SUP], Natalie E Dean[SUP] 4 [/SUP], Sarah Cobey[SUP] 5 [/SUP], Benjamin J Cowling[SUP] 6 [/SUP], Ye Shen[SUP] 1 [/SUP], Ted M Ross[SUP] 7 8 [/SUP], Andreas Handel[SUP] 1 2 [/SUP]
Affiliations
Introduction: Influenza continuously evolves to escape population immunity, which makes formulating a vaccine challenging. Antigenic differences between vaccine strains and circulating strains can affect vaccine effectiveness (VE). Quantifying the antigenic difference between vaccine strains and circulating strains can aid interpretation of VE, and several antigenic distance metrics have been discussed in the literature. Here, we compare how the predicted breadth of vaccine-induced antibody response varies when different metrics are used to calculate antigenic distance.
Methods: We analyzed data from a seasonal influenza vaccine cohort that collected serum samples from 2013/14 - 2017/18 at three study sites. The data include pre- and post-vaccination HAI titers to the vaccine strains and a panel of heterologous strains. We used that data to calculate four different antigenic distance measures between assay strains and vaccine strains: difference in year of isolation (temporal), p-Epitope (sequence), Grantham's distance (biophysical), and antigenic cartography distance (serological). We analyzed agreement between the four metrics using Spearman's correlation and intraclass correlation. We then fit Bayesian generalized additive mixed-effects models to predict the effect of antigenic distance on post-vaccination titer after controlling for confounders and analyzed the pairwise difference in predictions between metrics.
Results: The four antigenic distance metrics had low or moderate correlation for influenza subtypes A(H1N1), B/Victoria, and B/Yamagata. A(H3N2) distances were highly correlated. We found that after accounting for pre-vaccination titer, study site, and repeated measurements across individuals, the predicted post-vaccination titers conditional on antigenic distance and subtype were nearly identical across antigenic distance metrics, with A(H1N1) showing the only notable deviation between metrics.
Discussion: Despite moderate correlation among metrics, we found that different antigenic distance metrics generated similar predictions about breadth of vaccine response. Costly titer assays for antigenic cartography may not be needed when simpler sequence-based metrics suffice for quantifying vaccine breadth.
. 2025 Nov 14;21(11):e1013720.
doi: 10.1371/journal.pcbi.1013720. Online ahead of print. Different antigenic distance metrics generate similar predictions of influenza vaccine response breadth despite moderate correlation
W Zane Billings[SUP] 1 2 [/SUP], Yang Ge[SUP] 1 [/SUP], Amanda L Skarlupka[SUP] 3 [/SUP], Savannah L Miller[SUP] 1 2 [/SUP], Hayley Hemme[SUP] 1 2 [/SUP], Murphy John[SUP] 1 2 [/SUP], Natalie E Dean[SUP] 4 [/SUP], Sarah Cobey[SUP] 5 [/SUP], Benjamin J Cowling[SUP] 6 [/SUP], Ye Shen[SUP] 1 [/SUP], Ted M Ross[SUP] 7 8 [/SUP], Andreas Handel[SUP] 1 2 [/SUP]
Affiliations
- PMID: 41237217
- DOI: 10.1371/journal.pcbi.1013720
Introduction: Influenza continuously evolves to escape population immunity, which makes formulating a vaccine challenging. Antigenic differences between vaccine strains and circulating strains can affect vaccine effectiveness (VE). Quantifying the antigenic difference between vaccine strains and circulating strains can aid interpretation of VE, and several antigenic distance metrics have been discussed in the literature. Here, we compare how the predicted breadth of vaccine-induced antibody response varies when different metrics are used to calculate antigenic distance.
Methods: We analyzed data from a seasonal influenza vaccine cohort that collected serum samples from 2013/14 - 2017/18 at three study sites. The data include pre- and post-vaccination HAI titers to the vaccine strains and a panel of heterologous strains. We used that data to calculate four different antigenic distance measures between assay strains and vaccine strains: difference in year of isolation (temporal), p-Epitope (sequence), Grantham's distance (biophysical), and antigenic cartography distance (serological). We analyzed agreement between the four metrics using Spearman's correlation and intraclass correlation. We then fit Bayesian generalized additive mixed-effects models to predict the effect of antigenic distance on post-vaccination titer after controlling for confounders and analyzed the pairwise difference in predictions between metrics.
Results: The four antigenic distance metrics had low or moderate correlation for influenza subtypes A(H1N1), B/Victoria, and B/Yamagata. A(H3N2) distances were highly correlated. We found that after accounting for pre-vaccination titer, study site, and repeated measurements across individuals, the predicted post-vaccination titers conditional on antigenic distance and subtype were nearly identical across antigenic distance metrics, with A(H1N1) showing the only notable deviation between metrics.
Discussion: Despite moderate correlation among metrics, we found that different antigenic distance metrics generated similar predictions about breadth of vaccine response. Costly titer assays for antigenic cartography may not be needed when simpler sequence-based metrics suffice for quantifying vaccine breadth.