Abstract
ABSTRACTAdjuvants produce complex, but often subtle, effects on vaccine-induced immune responses that, nonetheless, play a critical role in vaccine efficacy. In-depth profiling of vaccine-induced cytokine, cellular, and antibody responses (“immunoprofiling”) combined with machine-learning holds the promise of identifying adjuvant-specific immune response characteristics that can guide rational adjuvant selection. Here, we profiled human immune responses induced by vaccines adjuvanted with two similar, clinically relevant adjuvants, AS01B and AS02A, and identified key distinguishing characteristics, or immune signatures, they imprint on vaccine-induced immunity. Samples for this side-by-side comparison were from malaria-naïve individuals who had received a recombinant malaria subunit vaccine (AMA-1) that targets the pre-erythrocytic stage of the parasite. Both adjuvant formulations contain the same immunostimulatory components, QS21 and MPL, thus this study reveals the subtle impact that adjuvant formulation has on immunogenicity. Adjuvant-mediated immune signatures were established through a two-step approach: First, we generated a broad immunoprofile (serological, functional and cellular characterization of vaccine-induced responses). Second, we integrated the immunoprofiling data and identify what combination of immune features was most clearly able to distinguish vaccine-induced responses by adjuvant using machine learning. The computational analysis revealed statistically significant differences in cellular and antibody responses between cohorts and identified a combination of immune features that was able to distinguish subjects by adjuvant with 71% accuracy. Moreover, the in-depth characterization demonstrated an unexpected induction of CD8+ T cells by the recombinant subunit vaccine, which is rare and highly relevant for future vaccine design.
Highlights
The goal of adjuvant research is to characterize the type of immune responses that different adjuvants and adjuvant formulations generate to enable rationale vaccine design whereby antigens are matched with optimal adjuvants
The objective of this study was to determine whether computational analysis can identify adjuvant-specific immune signatures for AS01B and/or AS02A in humans, identify immune measures associated with vaccine efficacy, and determine the effect of adjuvant formulation on vaccine-induced immune responses
The results revealed a large overlap in the immune signatures of AS01B and AS02A, which was anticipated since the two formulations contain the same immunostimulators
Summary
The goal of adjuvant research is to characterize the type of immune responses that different adjuvants and adjuvant formulations generate to enable rationale vaccine design whereby antigens are matched with optimal adjuvants. To gain useful insights into the immune signatures induced by adjuvants, it is necessary to perform immunoprofiling by collecting the broadest possible range of immune measures. While a useful starting point, these efforts have largely failed to predict vaccine efficacy or assist in the down-selection of vaccine formulations.[1,2,3] Surrogate markers or immune correlates of protection remain elusive for most vaccines and diseases[4] and uncovering these parameters could greatly assist in vaccine design and development. It is clear that a comprehensive understanding of vaccineinduced immunity is necessary but collecting and analyzing complex immune data sets poses a number of technical and analytical challenges
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