Identification of Immune Signatures of Novel Adjuvant Formulations Using Machine Learning.
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Abstract
Adjuvants have long been critical components of vaccines but the exact mechanisms of their action and precisely how they alter or enhance vaccine induced immune responses are often unclear In this study we used broad immunoprofiling of antibody cellular and cytokine responses combined with data integration and machine learning to gain insight into the impact of different adjuvant formulations on vaccine induced immune responses A Self Assembling Protein Nanoparticles SAPN presenting the malarial circumsporozoite protein CSP was used as a model vaccine adjuvanted with three different liposomal formulations liposome plus Alum ALFA liposome plus QS21 ALFQ and both ALFQA Using a computational approach to integrate the immunoprofiling data we identified distinct vaccine induced immune responses and developed a multivariate model that could predict the adjuvant condition from immune response data alone with 92 accuracy p 0 003 The data integration also revealed that commonly used readouts i e serology frequency of T cells producing IFN IL2 TNF missed important differences between adjuvants In summary broad immune profiling in combination with machine learning methods enabled the reliable and clear definition of immune signatures for different adjuvant formulations providing a means for quantitatively characterizing the complex roles that adjuvants can play in vaccine induced immunity The approach described here provides a powerful tool for identifying potential immune correlates of protection a prerequisite for the rational pairing of vaccines candidates and adjuvants