Identification of Immune Signatures of Novel Adjuvant Formulations Using Machine Learning

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-γ, IL2, TNFα) 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.

The COVID-19 Vaccine Race: Challenges and Opportunities in Vaccine Formulation

“…MD simulation has been applied in epitopecarrier fusion construction in HIV vaccine and malaria vaccine (19,20). The machine learning tool was used to predict the antigen-specific immune signatures in vaccines based on immune profiling data (21,22). Ong et al (2020) predicted possible vaccine targets of COVID-19 using a machine learning tool, including the non-structural protein (nsp3), a novel target that has not been tested for vaccines (23).…”

Predictive Markers of Immunogenicity and Efficacy for Human Vaccines

“…Indeed, Chaudhury et al showed that the sole use of such analysis is too reductive to identify differences between immune responses. Indeed, their predictive model of the malaria vaccine-induced immune response also integrates other immune parameters, such as IL-4 and IL-6 levels, which are variables of importance in different tissues such as blood or liver [32]. Years of immune system screening have demonstrated that immune responses to pathogens and vaccines are highly multifactorial and involve numerous diverse actors.…”

Immunoprofiling Correlates of Protection Against SHIV Infection in Adjuvanted HIV-1 Pox-Protein Vaccinated Rhesus Macaques

“…This information can be used to identify new correlates of protection from infection and lead to a more comprehensive understanding of vaccine mechanisms that underlie protection (35,36). Systems serology has been applied to search for immune features that best predict protection induced by HIV vaccines (37)(38)(39), malaria vaccines (40)(41)(42), and other vaccines (43,44). In the present study, we developed a systems serology pipeline that integrated both machine learning and Cox proportional hazards regression to analyze high-dimensional Fc array data.…”