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Comprehensive Data Integration Approach to Assess Immune Responses and Correlates of RTS,S/AS01-Mediated Protection From Malaria Infection in Controlled Human Malaria Infection Trials.

Authors :
Young WC
Carpp LN
Chaudhury S
Regules JA
Bergmann-Leitner ES
Ockenhouse C
Wille-Reece U
deCamp AC
Hughes E
Mahoney C
Pallikkuth S
Pahwa S
Dennison SM
Mudrak SV
Alam SM
Seaton KE
Spreng RL
Fallon J
Michell A
Ulloa-Montoya F
Coccia M
Jongert E
Alter G
Tomaras GD
Gottardo R
Source :
Frontiers in big data [Front Big Data] 2021 Jun 15; Vol. 4, pp. 672460. Date of Electronic Publication: 2021 Jun 15 (Print Publication: 2021).
Publication Year :
2021

Abstract

RTS,S/AS01 (GSK) is the world's first malaria vaccine. However, despite initial efficacy of almost 70% over the first 6 months of follow-up, efficacy waned over time. A deeper understanding of the immune features that contribute to RTS,S/AS01-mediated protection could be beneficial for further vaccine development. In two recent controlled human malaria infection (CHMI) trials of the RTS,S/AS01 vaccine in malaria-naïve adults, MAL068 and MAL071, vaccine efficacy against patent parasitemia ranged from 44% to 87% across studies and arms (each study included a standard RTS,S/AS01 arm with three vaccine doses delivered in four-week-intervals, as well as an alternative arm with a modified version of this regimen). In each trial, RTS,S/AS01 immunogenicity was interrogated using a broad range of immunological assays, assessing cellular and humoral immune parameters as well as gene expression. Here, we used a predictive modeling framework to identify immune biomarkers measured at day-of-challenge that could predict sterile protection against malaria infection. Using cross-validation on MAL068 data (either the standard RTS,S/AS01 arm alone, or across both the standard RTS,S/AS01 arm and the alternative arm), top-performing univariate models identified variables related to Fc effector functions and titer of antibodies that bind to the central repeat region (NANP6) of CSP as the most predictive variables; all NANP6-related variables consistently associated with protection. In cross-study prediction analyses of MAL071 outcomes (the standard RTS,S/AS01 arm), top-performing univariate models again identified variables related to Fc effector functions of NANP6-targeting antibodies as highly predictive. We found little benefit-with this dataset-in terms of improved prediction accuracy in bivariate models vs. univariate models. These findings await validation in children living in malaria-endemic regions, and in vaccinees administered a fourth RTS,S/AS01 dose. Our findings support a "quality as well as quantity" hypothesis for RTS,S/AS01-elicited antibodies against NANP6, implying that malaria vaccine clinical trials should assess both titer and Fc effector functions of anti-NANP6 antibodies.<br />Competing Interests: Authors EJ, FU-M, and MC are employees of the GSK group of companies. EJ, FU-M, and MC report ownership of shares and/or restricted shares of the GSK group of companies. FU-M and MC report grants from the Bill and Melinda Gates Foundation to the GSK group of companies during the conduct of this work. In addition, MC has a patent issued (Novel methods for inducing an immune response). GA is a founder of Systems Seromyx Inc. RG has received consulting income from Takeda Vaccines, speaker fees from Illumina and Fluidigm, research support from Janssen Pharmaceuticals, and declares ownership in Ozette Technologies and minor stock ownerships in 10X Genomics, Vir Biotechnology, Abcellera, BioNTech, and Sana Biotechnologies. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.<br /> (Copyright © 2021 Young, Carpp, Chaudhury, Regules, Bergmann-Leitner, Ockenhouse, Wille-Reece, deCamp, Hughes, Mahoney, Pallikkuth, Pahwa, Dennison, Mudrak, Alam, Seaton, Spreng, Fallon, Michell, Ulloa-Montoya, Coccia, Jongert, Alter, Tomaras and Gottardo.)

Details

Language :
English
ISSN :
2624-909X
Volume :
4
Database :
MEDLINE
Journal :
Frontiers in big data
Publication Type :
Academic Journal
Accession number :
34212134
Full Text :
https://doi.org/10.3389/fdata.2021.672460