Role of Egg-adaptation Mutations in Low Influenza A(H3N2) Vaccine Effectiveness During the 2012–2013 Season
Notice bibliographique
Résumé
To the Editor—The egg-adapted A(H3N2) vaccine component IVR-165 was associated with low influenza vaccine effectiveness (VE approximately 40%) during the 2012–2013 season [1]. IVR-165 bore 3 amino acid substitutions (H156Q, G186V, and S219Y) compared to the vaccine strain recommended by the World Health Organization (A/Victoria/361/2011). Notably, position 156 is located near the receptor binding site within immunodominant antigenic site B at the top of the hemagglutinin head and is 1 of just 7 positions associated with all major A(H3N2) antigenic cluster transitions since 1968 [2]. As such, the in vitro H156Q reversion that occurred with egg adaptation of the 2012–2013 vaccine strain is thought to have contributed to low influenza VE that season [1]. In their recent publication, Cobey et al hypothesize that vaccine mismatch due to egg-adaptation mutations should be evident as a different profile of influenza variants infecting vaccinated compared to unvaccinated people, whereas their sequence analysis detected no difference in 2012–2013 [3]. Their hypothesis, however, does not seem valid. By way of illustration, Cobey and co-authors have also proposed that egg-adaptation mutations (notably T160K, a loss of glycosylation) [4] played a key role in the low VE (<40%) against A(H3N2) in 2016–2017 and 2017–2018 [5–8]. However, they did not test their hypothesis of differing influenza variants by vaccine status for those particular seasons. In fact, viruses sequenced from Canadian VE study participants showed that there were also no differences in the profile of infecting influenza variants by vaccination status in 2016–2017 (n = 574) or 2017–2018 mid-season (n = 229; Table 1). We do not interpret those findings as ruling out a role for egg-adaptation mutations. Instead, and contrary to the assumption of Cobey et al, if egg-adaptation mutations affect antigenicity and reduce the immunogenicity of seasonal vaccine, then the infecting A(H3N2) strain should be independent of vaccination status—as observed in our data for 2016–2018 and also by Cobey et al for 2012–2013. Clade Distribution of A(H3N2) Viruses by Vaccination Status, Canadian Sentinel Practitioner Surveillance Network aSpecimens were tested for influenza viruses using reverse-transcriptase polymerase chain reaction at provincial public health reference laboratories as previously described [5, 7]. Genetic characterization of the hemagglutinin was attempted on all influenza-positive original specimens collected from Canadian Sentinel Practitioner Surveillance Network patients using Sanger sequencing. Phylogenetic analysis was conducted based on nucleotide sequence using the approximate likelihood method to determine clade distribution and identify major genetic clusters (or “parent” groups) in conjunction with published reports. See Supplementary Materials for more details and references related to sequence analysis. bVaccination status ascertained as per usual based on patient self-report and sentinel practitioner documentation. Patients who self-reported receipt of ≥1 dose of the current season’s influenza vaccine ≥2 weeks before onset of influenza-like illness (ILI) were considered vaccinated; those vaccinated <2 weeks before ILI onset were excluded. cP values based on χ2 test comparing the proportion of viruses within the specified clade/variant vs all other clades/variants among vaccinated vs unvaccinated participants. dMethods as per [5], but including viruses with specimen collection dates spanning up to 30 April 2017. Associated GenBank sequence numbers for 564 of 574 included viruses are KY583507 to KY583727, MH216203–MH216328, MH216331–MH216445, and MH216447–MH216548. Ten sequences were of insufficient quality for GenBank submission but were sufficient for clade/variant determination based on clade-defining amino acid substitutions. eFisher’s exact test used where >25% of expected cell counts were <5. fMethods as per [7]. Associated GenBank sequence numbers of included viruses are MG889597–MG889825. Clade Distribution of A(H3N2) Viruses by Vaccination Status, Canadian Sentinel Practitioner Surveillance Network aSpecimens were tested for influenza viruses using reverse-transcriptase polymerase chain reaction at provincial public health reference laboratories as previously described [5, 7]. Genetic characterization of the hemagglutinin was attempted on all influenza-positive original specimens collected from Canadian Sentinel Practitioner Surveillance Network patients using Sanger sequencing. Phylogenetic analysis was conducted based on nucleotide sequence using the approximate likelihood method to determine clade distribution and identify major genetic clusters (or “parent” groups) in conjunction with published reports. See Supplementary Materials for more details and references related to sequence analysis. bVaccination status ascertained as per usual based on patient self-report and sentinel practitioner documentation. Patients who self-reported receipt of ≥1 dose of the current season’s influenza vaccine ≥2 weeks before onset of influenza-like illness (ILI) were considered vaccinated; those vaccinated <2 weeks before ILI onset were excluded. cP values based on χ2 test comparing the proportion of viruses within the specified clade/variant vs all other clades/variants among vaccinated vs unvaccinated participants. dMethods as per [5], but including viruses with specimen collection dates spanning up to 30 April 2017. Associated GenBank sequence numbers for 564 of 574 included viruses are KY583507 to KY583727, MH216203–MH216328, MH216331–MH216445, and MH216447–MH216548. Ten sequences were of insufficient quality for GenBank submission but were sufficient for clade/variant determination based on clade-defining amino acid substitutions. eFisher’s exact test used where >25% of expected cell counts were <5. fMethods as per [7]. Associated GenBank sequence numbers of included viruses are MG889597–MG889825. Cobey et al further argue that, unlike anti-sera drawn from naive ferrets, anti-sera collected from adults vaccinated with the egg-adapted IVR-165 do not distinguish it from the recommended vaccine strain or circulating clade 3C.2 or 3C.3 viruses [3]. They report titers pre-vaccination and fold changes post-vaccination that were highly correlated across these test viruses. However, their correlations were driven by a majority of titers that started low and showed minimal or no vaccine-induced change. Their serologic analyses pooled just 28 adults aged 30–40 years and 33 adults aged 65–87 years. Although their figure 2 does not permit exact quantification owing to overlapping pairs and missing data, most sera displayed a <4-fold rise in vaccine-induced titers and a substantial proportion showed a <2-fold rise (within the margin of error of the dilutional hemagglutination inhibition assay) [9]. Although 20/56 (36%) participants seroconverted to IVR-165, this pooled finding is also difficult to interpret in the context of conventional immunogenicity thresholds for annual vaccine approval requiring seroconversion in at least 40% of young adults and 30% of elderly adults [10]. Either way, without the comparator of sera drawn from adults vaccinated with cell culture–based (or other non-egg–based) vaccine, the serologic findings presented by Cobey et al do not resolve a role for egg-adaptation mutations. Ultimately, egg-adaptation mutations that result in altered antigenicity and poor immunological responses (including minimal boosting of cross-reactive antibody) are not mutually exclusive phenomena. As we have underscored previously, more definitive investigations are needed to understand how these alterations may interact with other agent-host factors to modulate VE, including variation in priming epochs, birth cohort effects, and underlying immunological landscapes [11]. Supplementary materials are available at Clinical Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author. Acknowledgments. The authors gratefully acknowledge Tracy Chan, Rebecca Hickman, Suzana Sabaiduc, and Catharine Chambers at the British Columbia Centre for Disease Control for their contributions to virus sequencing and genomic analyses. The authors also acknowledge the contribution of sentinel sites and the virologic and epidemiologic contribution of investigators of the Canadian Sentinel Practitioner Surveillance Network (SPSN) for the specimens and data included in the sequence analysis presented. Financial support. Funding of the Canadian SPSN was provided by the British Columbia Centre for Disease Control, Alberta Health and Wellness, Public Health Ontario, Ministère de la santé et des services sociaux du Québec, and l’Institut national de santé publique du Québec for the 2016–2017 and 2017–2018 seasons, and additionally by the Public Health Agency of Canada for the 2016–2017 season. Potential conflicts of interest. G. D. S. has received grants for investigator-initiated studies unrelated to pneumococcal vaccine from GSK and Pfizer and provided paid expert testimony for the Ontario Nurses Association, the Quebec Ministry of Justice, and GSK. The remaining author: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,005 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».