Different antigenic distance metrics generate similar predictions of influenza vaccine response breadth despite moderate correlation

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.

We analyzed data from a seasonal influenza vaccine cohort which 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.

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.

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. Competing Interest Statement BJC has consulted for AstraZeneca, Fosun Pharma, GlaxoSmithKline, Haleon, Moderna, Novavax, Pfizer, Roche, and Sanofi Pasteur. None of these companies were involved in the formulation of the study or the decision to publish or conduct the study. All other authors declare no potential conflicts of interest. Funding Statement NED: partial funding from NIH contract(s)/grant(s) R01-AI139761. TMR: partial funding from the Georgia Research Alliance as an Eminent Scholar. AH: partial funding from NIH contract(s)/grant(s) U01AI150747, R01AI170116, and 75N93019C00052. YS: partial funding from NIH contract(s)/grant(s) R35GM146612, R01AI170116, and 75N93019C00052. SC: partial funding from NIH contract(s)/grant(s) R01AI170116. All other authors: none declared Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: IRB of University of Georgia waived ethical approval for this work I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability All data and code included in the present work are available on GitHub (https://github.com/ahgroup/billings-comp-agdist-public) and Zenodo (DOI: 10.5281/zenodo.15522148).