Human Vaccines & Immunotherapeutics (Dec 2021)

Evolution of strain coverage by the multicomponent meningococcal serogroup B vaccine (4CMenB) in France

  • Eva Hong,
  • Aude Terrade,
  • Alessandro Muzzi,
  • Rosita De Paola,
  • Giuseppe Boccadifuoco,
  • Rita La Gaetana,
  • Ala-Eddine Deghmane,
  • Mariagrazia Pizza,
  • Laura Serino,
  • Muhamed-Kheir Taha

DOI
https://doi.org/10.1080/21645515.2021.2004055
Journal volume & issue
Vol. 17, no. 12
pp. 5614 – 5622

Abstract

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The 4CMenB, a protein-based vaccine, was licensed in Europe in 2013 against invasive meningococcal disease caused by serogroup B and is currently implemented in several countries although according to different national strategies. Isolate coverage estimation is required as vaccine-targeted antigens may vary among isolates over time. Several phenotypic and genotypic methods have been developed to predict strain coverage by scoring the expression and cross-reactivity of vaccine antigens using the Meningococcal Antigen Typing system (MATS), by the genetic correlation of alleles encoding these antigens and MATS expression data (gMATS) and by the Meningococcal Deduced Vaccine Antigen Reactivity (MenDeVAR). We applied these approaches on meningococcal B isolates in France and compared two epidemiological years, 2013–2014 and 2018–2019. A strong correlation was observed between MATS data that were generated for the year 2013–2014 and the gMATS data extracted from whole genome sequencing. gMATS and MenDeVAR were next used to compare the two years. Using gMATS, the overall coverage was 77.2% (lower limit (LL)-upper limit (UL) 66.7–87.7) and 70.7% (LL-UL 61.5–80.0) for the two years, respectively. The reduction in coverage between the two years is mainly driven by the reduction of alleles exactly matching the vaccine antigens. A high number of unpredictable isolates was observed using the MenDeVAR and was due to lack of MATS information for new or rare alleles in particular for the year 2018–2019. Our data underline the need of continuous surveillance of strain coverage and the importance of generating phenotypic MATS data to update the genetic approaches of prediction.

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