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

TL;DR

Different ways to measure how much flu strains differ give similar predictions about vaccine effectiveness, suggesting simpler methods can be used.

Contribution

The study shows that various antigenic distance metrics yield similar vaccine response predictions despite low correlation.

Findings

Four antigenic distance metrics showed similar predictions of vaccine-induced antibody response breadth.

A(H3N2) was the only subtype with notable deviation between metrics.

Simpler sequence-based metrics may suffice over costly serological methods for predicting vaccine breadth.

Abstract

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 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.

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(H3N2) showing the only notable deviation between metrics, despite higher agreement for that subtype.

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.

Influenza viruses change rapidly, so designing vaccines that remain effective is difficult. Small differences between the strains in the vaccine and strains in circulation can reduce protection. We can use a variety of methods to measure how “different” two strains are, but these methods can disagree.

We compared four ways of measuring these differences (genetic, biochemical, antigenic cartography, and time-based). Using immunological data from several flu seasons, we measured strain differences four ways. Then, we compared the relationship between immunogenicity and distance for each method. Our comparisons used a causal framework so we can identify valid conclusions from observational data.

We found that the four measures did not always agree with each other. But, the metrics produced similar predictions about the breadth of immune response to vaccination. Thus, complex and expensive laboratory tests may not always be necessary. Many studies could use simpler methods to save time and money. These results may aid in evaluation of future influenza vaccines

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