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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.

Publication ,  Journal Article
Young, WC; Carpp, LN; Chaudhury, S; Regules, JA; Bergmann-Leitner, ES; Ockenhouse, C; Wille-Reece, U; deCamp, AC; Hughes, E; Mahoney, C ...
Published in: Front Big Data
2021

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.

Duke Scholars

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Published In

Front Big Data

DOI

EISSN

2624-909X

Publication Date

2021

Volume

4

Start / End Page

672460

Location

Switzerland

Related Subject Headings

  • 4609 Information systems
  • 4605 Data management and data science
 

Citation

APA
Chicago
ICMJE
MLA
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Young, W. C., Carpp, L. N., Chaudhury, S., Regules, J. A., Bergmann-Leitner, E. S., Ockenhouse, C., … Gottardo, R. (2021). 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. Front Big Data, 4, 672460. https://doi.org/10.3389/fdata.2021.672460
Young, William Chad, Lindsay N. Carpp, Sidhartha Chaudhury, Jason A. Regules, Elke S. Bergmann-Leitner, Christian Ockenhouse, Ulrike Wille-Reece, et al. “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.Front Big Data 4 (2021): 672460. https://doi.org/10.3389/fdata.2021.672460.
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. 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. Front Big Data. 2021;4:672460.

Published In

Front Big Data

DOI

EISSN

2624-909X

Publication Date

2021

Volume

4

Start / End Page

672460

Location

Switzerland

Related Subject Headings

  • 4609 Information systems
  • 4605 Data management and data science