Antibody Fab‐Fc properties outperform titer in predictive models of SIV vaccine‐induced protection

• Main Authors: Srivamshi Pittala, Kenneth Bagley, Jennifer A Schwartz, Eric P Brown, Joshua A Weiner, Ilia J Prado, Wenlei Zhang, Rong Xu, Ayuko Ota‐Setlik, Ranajit Pal, Xiaoying Shen, Charles Beck, Guido Ferrari, George K Lewis, Celia C LaBranche, David C Montefiori, Georgia D Tomaras, Galit Alter, Mario Roederer, Timothy R Fouts, Margaret E Ackerman, Chris Bailey‐Kellogg
• Format: Article
• Language: English
• Published: Wiley 2019-05-01
• Series: Molecular Systems Biology
• Subjects: antibody effector function; biomarker identification; HIV; protection modeling; systems serology
• Online Access: https://doi.org/10.15252/msb.20188747

Abstract Characterizing the antigen‐binding and innate immune‐recruiting properties of the humoral response offers the chance to obtain deeper insights into mechanisms of protection than revealed by measuring only overall antibody titer. Here, a high‐throughput, multiplexed Fab‐Fc Array was employed to profile rhesus macaques vaccinated with a gp120‐CD4 fusion protein in combination with different genetically encoded adjuvants, and subsequently subjected to multiple heterologous simian immunodeficiency virus (SIV) challenges. Systems analyses modeling protection and adjuvant differences using Fab‐Fc Array measurements revealed a set of correlates yielding strong and robust predictive performance, while models based on measurements of response magnitude alone exhibited significantly inferior performance. At the same time, rendering Fab‐Fc measurements mathematically independent of titer had relatively little impact on predictive performance. Similar analyses for a distinct SIV vaccine study also showed that Fab‐Fc measurements performed significantly better than titer. These results suggest that predictive modeling with measurements of antibody properties can provide detailed correlates with robust predictive power, suggest directions for vaccine improvement, and potentially enable discovery of mechanistic associations.